arXiv CSopen access

DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation

D EEP G UARD: Secure Code Generation via Multi-Layer Semantic Aggregation Li Huang1 , Zhongxin Liu2 , Yifan Wu3 , Tao Yin1 , Dong Li1 , Jichao Bi1 , Nankun Mu1 * , Hongyu Zhang1 , Meng Yan1 1 Chongqing University, 3 Peking University 2 The State Key Laboratory of Blockchain and Data Security, Zhejiang University {lee.h, lidong, bjc, nankun.mu, hyzhang, mengy}@cqu.edu.cn [email protected] [email protected], [email protected] 1.2

arXiv:2604.09089v1 [cs.SE] 10 Apr 2026

Large Language Models (LLMs) for code generation can replicate insecure patterns from their training data. To mitigate this, a common strategy for security hardening is to finetune models using supervision derived from the final transformer layer. However, this design may suffer from a final-layer bottleneck: vulnerability-discriminative cues can be distributed across layers and become less detectable near the output representations optimized for next-token prediction. To diagnose this issue, we perform layer-wise linear probing. We observe that vulnerability-related signals are most detectable in a band of intermediateto-upper layers yet attenuate toward the final layers. Motivated by this observation, we introduce D EEP G UARD, a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module. The aggregated signal powers a dedicated security analyzer within a multi-objective training objective that balances security enhancement and functional correctness, and further supports a lightweight inference-time steering strategy. Extensive experiments across five code LLMs demonstrate that D EEP G UARD improves the secureand-correct generation rate by an average of 11.9% over strong baselines such as SVEN. It also preserves functional correctness while exhibiting generalization to held-out vulnerability types. Our code is public at § https: //github.com/unknownhl/DeepGuard.

1

Introduction

Large Language Models (LLMs) have demonstrated exceptional performance in various programming-related tasks, particularly in generating functionally correct code based on user-provided prompts (Nijkamp et al., 2022; Yan et al., 2025). This capability has led to their * Corresponding author.

Probability of Vulnerability

Abstract

Vulnerable Inputs Secure Inputs Semantic Rich Region

Peak Detection Confidence (Layer 9)

1.0 0.8 0.6 0.4 0.2 0.0

0

5

10

15

20

Transformer Layers

25

30

Figure 1: Layer-wise diagnostic evidence on SeedCoder-8B. We train a linear probe on each transformer layer to detect vulnerable patterns and report the probe confidence across layers. The vulnerabilitydiscriminative signal peaks in intermediate-to-upper layers and attenuates toward the final layers.

widespread adoption in real-world development environments. For example, GitHub’s Copilot is reported to assist in generating up to 46% of the code on its platform (Dohmke, 2023). However, this rapid integration introduces a critical and persistent security risk. The models’ power is rooted in their training on vast amounts of public code, which is a double-edged sword: the models also learn and can replicate the insecure coding patterns common in that data. Pearce et al. (2025) found that approximately 40% of code generated by Copilot contained vulnerabilities. Compounding this issue, user studies confirm that developers often fail to identify these AI-generated flaws (Mohsin et al., 2024; Majdinasab et al., 2024). Consequently, while code LLMs accelerate development, they risk introducing vulnerabilities into the software ecosystem (Basic and Giaretta, 2024), highlighting the urgent need for security hardening methods. To address this challenge, several defence mechanisms have been proposed. The first is inference-

(A) Single-Layer Guidance

(B) Multi-Layer Guidance Prompt

Prompt def process_data(user_input): query = "SELECT ... name =

def process_data(user_input): query = "SELECT ... name =

LLMS

LLMS

Layer 0

Prediction

Layer 1

Layer 1 logits distribution

… Layer N-1

…

Layer 2

Layer 0

DeepFusion Module Guided Prediction logits distribution

Layer N-2 '

+

?

Generated def process_data(user_input): query = "SELECT ... name = '" + user_input + "'"

Layer N-1

Generated

'

+

?

def process_data(user_input): query = "SELECT ... name = ?" db.execute(query, (user_input,))

Figure 2: Comparison of security guidance paradigms. (A) Single-layer guidance suffers from signal attenuation at the final layer. (B) D EEP G UARD (Ours) employs multi-layer aggregation to capture richer securitycritical cues distributed across upper layers.

time interventions, which treat the code LLM as a fixed black box. These methods range from automated prompt optimization (Nazzal et al., 2024; Zhang et al., 2024) to co-decoding with smaller models trained for security verification (Li et al., 2024). However, such methods do not adapt the model itself and typically rely on post-hoc feedback or surface-level patterns, which may be insufficient to correct a model’s insecure generation tendencies. A more powerful direction is model adaptation through training, including security-specific instruction tuning (He et al., 2024) and prefixtuning (He and Vechev, 2023). While effective, most of them share a critical limitation: they derive the training signal almost exclusively from the final transformer layer. We refer to this limitation as a final-layer bottleneck. Preventing insecure code often requires integrating diverse syntactic and semantic evidence. For example, identifying a potential SQL injection requires recognizing the syntactic pattern of string concatenation and reasoning about semantic properties such as untrusted data flow. Such evidence is known to be distributed hierarchically across transformer layers: shallower layers tend to capture structural syntax, while deeper layers encode more abstract semantics (Ma et al., 2024; Wan et al., 2022). Meanwhile, the final-layer representation is primarily optimized for next-token prediction rather than fine-grained vulnerability discrimination. As a result, features useful for separating vulnerable from secure patterns can become less separable near the output layer. Figure 1 provides diagnostic evidence consistent with this hypothesis: probe-detectable vulnerability signals attenuate toward the final layers.

To address this limitation, we introduce D EEP G UARD, a hybrid framework that combines model adaptation with a lightweight inference-time steering strategy. D EEP G UARD moves beyond finallayer-only analysis by introducing an attentionbased multi-layer aggregator (Figure 2B). The aggregator dynamically fuses hidden states from multiple upper layers, producing an aggregated representation that is more sensitive to security-critical cues distributed across the layers of the model. This representation powers a dedicated security analyzer within a multi-objective training framework that co-optimizes security enhancement and functional correctness. During inference, D EEP G UARD computes a context-aware security bias once from the prompt and applies it to logits during generation, helping steering the code away from vulnerable patterns without per-step re-evaluation overhead. We evaluate D EEP G UARD on both security enhancement and functional correctness across five strong code LLMs. The results show that D EEP G UARD achieves a favourable balance between these competing objectives. For example, on Qwen2.5-Coder-3B, a strong baseline (SVEN) achieves a sec-pass@1 score of 70.47%. After applying D EEP G UARD, this score increases to 80.76% while maintaining functional correctness (pass@1 of 86.65%, close to the original model). Across models, D EEP G UARD improves the secureand-correct generation metric by 11.9% on average over SVEN, and exhibits strong generalization to vulnerability types held out during training within the benchmark. In summary, our contributions are: • We provide diagnostic evidence that vulnerability signals attenuate at the final transformer layer, highlighting the limitations of final-layer-only supervision. • We propose D EEP G UARD, a framework incorporating attention-based multi-layer aggregation and multi-objective training to leverage internal model representations for security. • We demonstrate through extensive evaluation that D EEP G UARD achieves superior security performance and generalization across multiple models compared to baselines.

2

Related Work

Security of LLM-generated Code Large language models are known to generate vulnerable code (Pearce et al., 2025; He et al., 2024; Asare

et al., 2024; Huang et al., 2025). Foundational studies established the systematic evaluation of these models using industry-standard tools like GitHub CodeQL (GitHub, 2023) to detect Common Weakness Enumerations (CWEs) (MITRE, 2023). Pioneering work by Pearce et al. (2025) used this approach to find that a significant portion of AIgenerated code contains exploitable vulnerabilities, a finding later confirmed by numerous others (Khoury et al., 2023; Siddiq and Santos, 2022; Fakih et al., 2025; de Fitero-Dominguez et al., 2024). The demonstrated security risks have motivated two main categories of defences. Inferencetime methods (Fu et al., 2024), such as prompt optimization (Nazzal et al., 2024) or co-decoding (Li et al., 2024), offer flexibility but are limited in their ability to correct a model’s underlying insecure tendencies. In contrast, training-time adaptation methods directly modify the model’s behaviour through security-focused fine-tuning (He et al., 2024; Huang et al., 2026) or prefix-tuning (He and Vechev, 2023). While powerful, these methods share a critical limitation: they almost exclusively use the final-layer hidden states of the model as their primary training signal. This “point” representation creates an information bottleneck, ignoring the rich context distributed across the model’s layers. Our work addresses this limitation within the model adaptation paradigm. Multi-Layer Feature Aggregation It is wellestablished that the internal representations of Transformer-based models are hierarchical. In the domain of source code, probing studies have confirmed that different layers specialize in capturing distinct features: lower layers tend to encode local syntactic structures, while upper layers learn more abstract semantic properties (Ma et al., 2024; Wan et al., 2022). However, the distributed information available in the intermediate layers of code LLMs remains largely untapped by prior security hardening methods. Our work is the first to propose and evaluate a learned, multi-layer aggregation strategy for this purpose, demonstrating that the resulting “regional” representation provides a more robust signal for identifying and mitigating vulnerabilities compared to existing final-layer-only approaches.

3

DeepGuard

This section introduces D EEP G UARD, a trainingand-inference framework designed to mitigate the common limitation of security adaptation meth-

ods that derive supervision primarily from the final transformer layer. Motivated by our diagnostic analysis (Figure 1), the key is to leverage securityrelevant cues that can be distributed in intermediateto-upper layers, rather than relying on a single finallayer vector. D EEP G UARD comprises two components: (i) a multi-objective adaptation stage that updates the code LLM using LoRA, and (ii) a lightweight guided inference stage that applies a prompt-conditioned security bias during generation. We denote the base code LLM as M with parameters θ, and the adapted model as M′ with parameters θ′ = θ + ∆θ, where ∆θ denotes the effective parameter update induced by the trainable LoRA modules. 3.1

Multi-Layer Representation Aggregation

We aim to construct a representation that provides a stronger basis for security analysis than using a single final-layer state alone. Given an input token sequence x = (t1 , t2 , . . . , tS ), the adapted model M′ produces hidden states from L transformer layers, {H1 , H2 , . . . , HL }, where Hi ∈ RS×D and D is the hidden dimension. To capture distributed security-relevant signals, we restrict our focus to the top N layers rather than the final layer alone. Specifically, we aggregate the hidden states from the set Htop-N = {HL−N +1 , . . . , HL }. Attention-based fusion. We introduce an aggregator fagg to fuse Htop-N into a single representation Hagg ∈ RS×D . Concretely, for token position j, we stack its layer-wise states as (j) (j) h(j) = [hL−N +1 , . . . , hL ]⊤ ∈ RN ×D . We com(j)

pute the fused state hagg using an attention module. Specifically, we use the mean of the stacked states P (j) as a summary query, h̄(j) = N1 L i=L−N +1 hi , and set Q(j) = h̄(j) WQ , K(j) = h(j) WK , and V(j) = h(j) WV , where WQ , WK , WV ∈ RD×D . The fused state is then computed as ! ⊤ Q(j) K(j) (j) √ hagg = Softmax V(j) . (1) D Intuitively, h̄(j) provides a stable “consensus” summary across layers, and attention then assigns higher weight to layer views that are most informative for the downstream analyzer. 3.2

Training: Multi-Objective Adaptation

We adapt the base model using LoRA (Hu et al., 2022) on paired data D = {(xvul , xsec )}, where

Vulnerable Code

def process_data(user_input): query = "SELECT ... name =

Multi-Layer Fusion

Layer 0 Layer 1

Layer 1

𝑠𝑠𝑠𝑠𝑠𝑠 𝑃𝑃𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

Layer 1

… Layer N-1

Security Score

Layer N-1

Base LLMs Layer 0

𝑠𝑠𝑠𝑠𝑠𝑠 𝑃𝑃𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏

𝑠𝑠𝑠𝑠𝑠𝑠 𝑃𝑃𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

ℒ𝑔𝑔𝑔𝑔𝑔𝑔 CrossEntropy ℒ𝑘𝑘𝑘𝑘 KL Divergence

Security Analyzer

Layer N-2

score_vul score_sec

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…

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ℒ𝑠𝑠𝑠𝑠𝑠𝑠 Margin Loss

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Security Analyzer

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Lora

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Secure Code

def pascal_case( value: str) -> str:\n return stringcase.p ascalcase(_s anitize(valu e))

Lora

Base LLMs

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Token Security Stats

(B) Inference Phase

…

def pascal_case( value: str) -> str:\n return stringcase.p ascalcase(va lue)

(A) Training Phase

Guided Logits

'

+

?

Original Logits Security Logits Processor

Token Security Stats

def process_data(user_input): query = "SELECT ... name = ?" db.execute(query, (user_input,))

Figure 3: Overview of D EEP G UARD, depicting the multi-objective training phase and the guided inference phase.

xvul is a vulnerable snippet and xsec is its functionally equivalent secure counterpart. Our training objective balances three goals: encouraging secure behavior, preserving fluency, and maintaining functional correctness. Security and Contrastive Objective We introduce a security analyzer fsa parameterized by ϕsa . The analyzer consumes (i) the aggregated representation Hagg and (ii) a learned token-level security embedding Esec ∈ R|V |×Demb , where V is the vocabulary. The embedding provides a lightweight token prior that can complement contextual information in Hagg . Specific initialization and architectural details are provided in Appendix C.2. For an input sequence x, we compute per-token scores:   s(x) = fsa [Hagg ; femb (x)] ∈ [0, 1]S , (2) where femb is an embedding lookup and [·; ·] denotes concatenation along the hidden dimension, and the score at position i is denoted by si (x). In practice, fsa is a small MLP whose outputs are normalized to [0, 1] via a sigmoid function. To evaluate the sequence as a whole, we define the sequence-level security score as Pthe average of the token-level scores s̄(x) = S1 Si=1 si (x). Given a training pair (xvul , xsec ), we compute their respective sequence scores s̄vul and s̄sec . We then apply a margin-based contrastive loss to encourage separation, letting δs = s̄sec − s̄vul : Lsec = E(xvul ,xsec )∼D [max(0, ∆ − δs )],

(3)

where ∆ is a margin hyperparameter. This objective provides a direct training signal that prefers secure variants over their vulnerable counterparts under the analyzer. Preserving Fluency and Functionality To maintain language modeling ability, we include the standard next-token prediction loss on secure examples:   |xsec | X Lgen = −Exsec ∼D  log P (ti | t<i ; θ′ ) . (4) i=1

To reduce catastrophic forgetting, we further regularize the adapted distribution Pθ′ toward the frozen base model distribution Pθ using KL divergence:
Lkl = Exsec ∼D DKL Pθ ∥ Pθ′ xsec , (5) where DKL (Pθ ∥Pθ′ | x) denotes the KL divergence between Pθ (·|x) and Pθ′ (·|x). The final objective is a weighted sum: Ltotal = Lgen + wsec Lsec + wkl Lkl ,

(6)

where wsec and wkl balance security and preservation objectives. 3.3

Inference: Guided Secure Generation

While the training objective encourages secure behavior, inference-time steering can further reduce insecure outputs with minimal overhead. We refer to this mechanism—combining a lightweight token prior with prompt-conditioned logit biasing—as guided inference.

A lightweight token prior. We maintain a tokenlevel prior vector Tstats ∈ R|V | to capture the global empirical association of each token with secure versus vulnerable contexts. Concretely, during training, we update the entries in Tstats corresponding to the tokens present in each batch: we increase the scores for tokens appearing in secure samples and decrease them for those in vulnerable samples by a fixed step size. The values are finally clipped to [−1, 1] to ensure stability. This prior is not intended to be a calibrated vulnerability estimator, but serves as a weak distributional bias when combined with contextual signals. We provide a statistical analysis and semantic interpretation in Appendix F.3. Prompt-conditioned bias. Given an input prompt xprompt , we perform a single forward pass prompt to compute its aggregated representation Hagg and obtain per-token scores s(xprompt ) from the trained analyzer. We summarize the prompt by its mean score s̄prompt , which serves as a coarse indicator of the prompt’s security posture under the analyzer. We then compute a vocabulary-wide bias vector b ∈ R|V | : b = (1 − s̄prompt ) ·

Tstats , max(|Tstats |) + ϵ

(7)

where normalization scales Tstats to a bounded range and ϵ ensures numerical stability. The factor (1 − s̄prompt ) ∈ [0, 1] modulates the bias strength, yielding stronger steering when the prompt appears more vulnerable under the analyzer. Logit biasing. At each decoding step i, we add the fixed bias to the model’s logits zi : z′i = zi + b.

(8)

We then sample ti ∼ Softmax(z′i ). This design avoids per-step re-evaluation by the analyzer and introduces only negligible overhead beyond standard decoding. We provide a theoretical FLOPs analysis in Appendix E.1 and report the empirical inference latency across models in Appendix F.2. Discussion. Our guided inference is intentionally lightweight and does not aim to replace stronger but more expensive search-time defences (e.g., iterative re-scoring). Instead, it provides a low-cost complement that empirically improves security under the same decoding budget.

4

Experiments

4.1

Setup

Models and Benchmarks. We evaluate D EEP G UARD on a diverse set of recent open-source code LLMs spanning multiple families and model scales, including Qwen2.5-Coder (3B, 7B) (Hui et al., 2024), DeepSeek-Coder (1.3B, 6.7B) (Guo et al., 2024), and Seed-Coder (8B) (Zhang et al., 2025). Our experiments follow a widely-used secure code generation benchmark and evaluation protocol introduced by He and Vechev (2023) and Fu et al. (2024), enabling direct comparison under the same scenario-based setup. Dataset statistics and unit test specifications are provided in Appendix A. Baselines. We compare against representative defenses from different paradigms: two strong whitebox adaptation baselines SVEN (He and Vechev, 2023) and SafeCoder (He et al., 2024), two strong inference-time defenses CoSec (Li et al., 2024) and CodeGuard+ (Fu et al., 2024), and a simple prompt-based safety instruction baseline. We also report the Base Model without adaptation. All methods are evaluated under the same prompts and decoding budget. Metrics. We adopt the comprehensive evaluation protocol used by Fu et al. (2024). We use securepass@k as the primary utility metric, and additionally report sec@kpass as a diagnostic metric for held-out vulnerability types, which isolates security among correct generations. We also report pass@k and SVEN-SR for completeness. Formal definitions are included in Appendix B. Implementation Details. We implement D EEP G UARD using LoRA for all model variants. Unless stated otherwise, we maintain a consistent hyperparameter configuration across different model families. For inference, we adopt a low-temperature sampling strategy to favor deterministic code generation. A comprehensive listing of configurations is provided in Appendix C.1 and hyperparameter sensitivity is shown in Appendix E. 4.2

Main Results

Table 1 shows the main results across five code LLMs. D EEP G UARD improves security-oriented metrics while maintaining competitive functional correctness. We highlight several observations below. For a granular performance breakdown across specific CWE scenarios, see Figures 12 and 13.

Table 1: Performance comparison across different models and methods. All metrics are reported as percentages (%). “Imp. (%)” columns show the relative improvement of D EEP G UARD (Ours) over other baselines. Model

Method

pass@1 (↑)

sec@1pass (↑)

sec-pass@1 (↑)

SVEN-SR (↑)

Value

Imp.(%)

Value

Imp.(%)

Value

Imp.(%)

Value

Imp.(%)

Qwen2.5Coder-3B

Base Prompt SVEN SafeCoder CoSec CodeGuard+ Ours

91.00 85.41 83.00 63.94 82.06 88.82 86.65

-4.78 +1.45 +4.40 +35.52 +5.59 -2.44 –

76.47 72.93 84.90 82.34 76.85 80.13 93.21

+21.89 +27.81 +9.79 +13.20 +21.29 +16.32 –

69.59 62.29 70.47 52.65 63.06 71.18 80.76

+16.05 +29.65 +14.60 +53.39 +28.07 +13.46 –

77.95 75.84 82.60 87.02 78.35 81.37 94.11

+20.73 +24.09 +13.93 +8.15 +20.11 +15.66 –

Qwen2.5Coder-7B

Base Prompt SVEN SafeCoder CoSec CodeGuard+ Ours

80.94 84.35 81.00 79.76 80.82 82.06 83.18

+2.77 -1.39 +2.69 +4.29 +2.92 +1.36 –

76.45 83.26 75.45 84.51 79.33 85.66 88.19

+15.36 +5.92 +16.89 +4.35 +11.17 +2.95 –

61.88 70.24 61.12 67.41 64.12 70.29 73.35

+18.54 +4.43 +20.01 +8.81 +14.39 +4.35 –

78.36 84.53 76.24 86.69 80.44 87.18 89.21

+13.85 +5.54 +17.01 +2.91 +10.90 +2.33 –

DeepSeekCoder-1.3B

Base Prompt SVEN SafeCoder CoSec CodeGuard+ Ours

81.65 83.24 81.88 65.88 81.76 82.35 81.06

-0.72 -2.62 -1.00 +23.04 -0.86 -1.57 –

69.81 70.32 74.50 79.20 72.37 92.86 84.91

+21.63 +20.75 +13.97 +7.21 +17.33 -8.56 –

57.00 58.53 61.00 52.18 59.18 76.47 68.82

+20.74 +17.58 +12.82 +31.89 +16.29 -10.00 –

69.83 69.71 77.87 77.16 71.64 88.24 87.71

+25.61 +25.82 +12.64 +13.67 +22.43 -0.60 –

DeepSeekCoder-6.7B

Base Prompt SVEN SafeCoder CoSec CodeGuard+ Ours

91.35 82.06 85.71 68.71 84.24 87.59 88.47

-3.15 +7.81 +3.22 +28.76 +5.02 +1.00 –

75.27 78.71 79.41 84.59 73.81 86.57 79.52

+5.65 +1.03 +0.14 -5.99 +7.74 -8.14 –

68.76 64.59 68.06 58.12 62.18 75.82 70.35

+2.31 +8.92 +3.36 +21.04 +13.14 -7.21 –

76.47 76.61 82.34 88.12 75.21 87.58 81.82

+7.00 +6.80 -0.63 -7.15 +8.79 -6.58 –

SeedCoder-8B

Base Prompt SVEN SafeCoder CoSec CodeGuard+ Ours

84.88 86.12 83.76 81.06 77.41 77.06 86.59

+2.01 +0.55 +3.38 +6.82 +11.86 +12.37 –

72.77 86.48 88.62 92.31 81.16 82.82 93.21

+28.09 +7.78 +5.18 +0.97 +14.85 +12.55 –

61.76 74.47 74.24 74.82 62.82 63.82 80.71

+30.68 +8.38 +8.71 +7.87 +28.48 +26.47 –

76.30 82.55 85.94 93.44 82.16 79.56 93.21

+22.16 +12.91 +8.46 -0.25 +13.45 +17.16 –

Security enhancement under end-to-end utility. We first focus on sec-pass@1, which measures the probability that the generated code is both secure and functionally correct. We observe that D EEP G UARD achieves the strongest or near-strongest sec-pass@1 across all evaluated models in Table 1. In particular, on Qwen2.5-Coder-3B, D EEP G UARD improves sec-pass@1 from 70.47% (SVEN) to 80.76%, indicating a substantial gain under the same benchmark setting. Averaged across models, D EEP G UARD yields consistent improvements over both SVEN and CoSec on sec-pass@1. Functional correctness is largely preserved. Security hardening methods can trade off functional correctness (Dai et al., 2025). In Table 1, D EEP G UARD generally maintains strong pass@1, often close to the base model and competitive with other defenses. For example, on DeepSeek-Coder6.7B, D EEP G UARD attains pass@1 of 88.47%, higher than SVEN (85.71%) and CoSec (84.24%).

We also note that in a few cases the relative ordering among methods can vary by model family, suggesting that the security–utility trade-off may be model-dependent in practice. Security among correct solutions. To isolate security performance conditioned on correctness, we examine sec@1pass . D EEP G UARD achieves the best sec@1pass for all five models in Table 1, suggesting that when the model produces a correct solution, D EEP G UARD increases the likelihood that the solution is secure. Notably, the prompt-based baseline can be competitive on some models (e.g., Seed-Coder-8B), highlighting that instruction-level safety prompting can already capture part of the benefit in this benchmark. However, D EEP G UARD remains consistently stronger on sec@1pass . Generalization to held-out vulnerability types. A rigorous test of any security hardening method is its ability to handle threats not seen during training. This evaluation (He and Vechev, 2023) comprises

sec@1pass (%)

Base 100 95 90 85 80 75 70

Prompt

SVEN

SafeCoder

CoSec

92.5 90.6 90.1 86.6 84.6 84.5

91.9

88.5 82.4 84.4 81.8

71.8

Qwen2.5-Coder-3B

CodeGuard+

D EEP G UARD

100

99.8

97.1

89.7

85.9 79.5 78.8 77.2 73.2

100 87.4 85.5 83.1 76.2 75.2 75.1 77

Qwen2.5-Coder-7B DeepSeek-Coder-1.3B DeepSeek-Coder-6.7B

99.7 93.3 90.1 88.1 87.1 79.9

Seed-Coder-8B

Figure 4: sec@1pass on CWEs that do not appear in the training dataset. Table 2: Ablation study and sensitivity analysis of D EEP G UARD on Seed-Coder-8B. The green row denotes the default D EEP G UARD (attn.Pool N = 4). Sections with pale green headers analyze specific components: training objectives (Loss), inference mechanisms, and multi-layer aggregation strategies. VARIANT

pass@1

sec@1pass

sec-pass@1

SVEN-SR

D EEP G UARD (N = 4)

86.59

93.21

80.71

93.21

Loss Component Ablation (-) Lgen (Fluency) (-) Lkl (Stability) (-) Lsec (Security)

84.53 74.12 64.94

93.04 98.49 91.03

78.65 73.00 59.12

93.09 98.84 92.80

Inference Strategy Ablation (-) Guided Inference (-) Prompt Condition (-) Random Token Stats

84.76 82.59 70.18

72.52 80.98 87.01

61.47 66.88 61.06

76.21 84.16 90.30

Aggregation Strategy Last Layer (N = 1) Mean Pool (N = 4) Attn. Pool (N = 2)

82.65 84.00 86.00

89.25 93.00 93.07

73.76 78.12 80.24

90.25 94.05 93.04

12 testing scenarios covering 4 distinct CWEs, which were excluded from the training dataset. Figure 4 visualizes the results, using sec@1pass to measure the transfer of security knowledge. The results show that D EEP G UARD maintains high sec@1pass across all models, while SVEN exhibits a larger drop on some models (e.g., DeepSeek-Coder-1.3B). These results suggest that leveraging multi-layer representations can improve transfer to held-out vulnerability types. 4.3

Ablation Study and Sensitivity

We dissect D EEP G UARD to quantify the contributions of its training objectives, inference strategy, and aggregation design. Table 2 summarizes the results. Detailed definitions for each ablation variant are provided in Appendix C.3. Training objectives. Removing any term in the multi-objective objective degrades performance. Ablating the security contrastive term Lsec yields the largest drop in pass@1 (86.59% → 64.94%) and sec-pass@1 (80.71% → 59.12%). This sharp decline occurs because the inference phase continues to rely on the security analyzer. When without the supervision from Lsec , the untrained analyzer produces unreliable scores that result in “noisy

steering”, which may disrupt the decoding process. In contrast, removing the stability regularizer Lkl increases security scores but substantially harms pass@1, consistent with the role of KL regularization in constraining distribution shift during adaptation. Finally, omitting Lgen uniformly degrades metrics, suggesting that retaining the language modeling objective helps preserve generation fluency and stabilizes optimization. Guided inference. Disabling guided inference causes a sharp drop in security metrics, showing that inference-time steering acts as a practical safeguard in addition to training-time adaptation. Within guided inference, prompt conditioning (via s̄prompt ) improves precision beyond static token priors: removing prompt conditioning reduces secpass@1 (80.71% → 66.88%). Replacing token statistics with random priors further degrades performance, supporting that the learned priors carry meaningful distributional structure rather than acting as arbitrary noise. We further analyze the robustness of guided inference from two perspectives in Section 4.4. Aggregation strategy. Using only the final layer leads to the weakest performance among aggregation choices (sec-pass@1 = 73.76%), consistent with the “final-layer bottleneck” hypothesis. Mean pooling across top layers improves sec-pass@1 (78.12%), while attention-based aggregation yields the best overall performance (80.71%), suggesting that learnable, context-dependent weighting can better surface security-relevant cues. Increasing the aggregated depth beyond a moderate N shows diminishing returns (see Appendix E.1), so setting N = 4 by default is reasonable. 4.4

Robustness of Guided Inference

In this section, we examine guided inference from two perspectives: its potential interference with benign code generation, and its ability to adapt when security-relevant risks emerge later during decoding.

Table 3: Performance of HumanEval with or without D EEP G UARD’s guided inference.

Table 5: Cross-model summary of layer-wise probing. Model

Model

Method

Qwen2.5Coder-3B

Base Model D EEP G UARD w/o inference

52.4 56.0 62.4

– 62.5 69.9

– 64.3 71.8

– 66.0 73.2

Base Model DeepSeekD EEP G UARD Coder-1.3B w/o inference

34.8 24.5 29.4

– 28.9 34.3

– 30.2 36.1

– 31.6 38.3

Base Model D EEP G UARD w/o inference

77.4 72.1 79.6

– 77.4 84.1

– 79.2 85.3

– 81.0 86.3

SeedCoder-8B

pass@1 pass@5 pass@10 pass@25

Table 4: Latency of interval-based re-scoring for 300token generation. Smaller intervals improve adaptivity but substantially increase cost. Method Time (s) Tokens/sec Re-scores

Overhead

Default k = 64 k = 16 k=4 k=1

D EEP G UARD +38.7% +195.8% +825.4% +3372.2%

6.886 9.550 20.366 63.723 239.097

43.57 31.42 14.73 4.71 1.25

1 5 19 75 300

Potential systematic bias on benign tasks. Although the bias term in Eq. 7 is scaled by the prompt-level security score s̄prompt , it may still suppress tokens that are legitimate in benign contexts. To quantify this trade-off, we evaluate on HumanEval (Chen et al., 2021) and compare the base model, D EEP G UARD, and D EEP G UARD without guided inference. As shown in Table 3, D EEP G UARD without inference remains competitive with, and sometimes improves upon, the base model on general functional correctness. For example, on Qwen2.5-Coder-3B, pass@1 increases from 52.4% to 62.4%. In contrast, enabling guided inference reduces performance on DeepSeek-Coder1.3B and Seed-Coder-8B. This shows that benigntask interference mainly arises from the inferencetime token bias rather than from the training-time adaptation itself. Since guided inference is decoupled from the adapted weights, it can be disabled when general functional correctness is prioritized. Interval-based re-scoring. Our default inference design computes the security bias once from the prompt and reuses it throughout decoding. This choice is efficient, but cannot react to risks that emerge only after a longer generated prefix. To study this trade-off, we implement interval-based re-scoring, which refreshes the bias every k generated tokens. Table 4 shows a steep trade-off between efficiency and adaptivity. A moderate in-

#L Peak Pos. Ppeak → Pfinal layer (%)

Seed-Coder-8B 32 Qwen2.5-Coder-3B 36 DeepSeek-Coder-1.3B 24 DeepSeek-Coder-6.7B 32 Qwen2.5-Coder-7B 28

9 9 7 22 27

29 0.9995 → 0.8574 26 0.8900 → 0.3326 30 0.6607 → 0.4984 71 0.7951 → 0.5485 100 0.8754 → 0.8754

Rel. drop (%)

p

14.2 62.6 24.6 31.0 0.0

4.49 × 10−4 6.81 × 10−13 1.22 × 10−6 2.90 × 10−10 1.00

terval (k = 64) introduces only five re-scoring events and a 38.7% latency increase, offering a practical compromise between adaptivity and efficiency. However, the cost rises rapidly as k decreases: k = 16 already incurs 195.8% overhead, while per-step re-scoring is prohibitively expensive.

5

Analysis

5.1 Corroborating the Final-Layer Bottleneck Figure 1 illustrates a representative diagnosis on Seed-Coder-8B. To determine if this phenomenon generalizes, we apply the same layer-wise probing protocol to all five evaluated models. Table 5 summarizes the peak locations of vulnerabilitydiscriminative signals and their subsequent attenuation at the final layer. The results confirm that the final-layer bottleneck is prevalent: in four out of the five models, discriminative signals peak at intermediate layers (ranging from 26% to 71% relative depth) before dropping significantly by the output layer. The only exception is Qwen2.5-Coder-7B, which preserves its peak signal at the final layer. This substantial cross-model variance in peak signal depth demonstrates that the optimal securitysensitive representation is highly model-dependent, thereby strongly motivating multi-layer aggregation over single-layer reliance. Furthermore, Figure 5 reveals a highly nonuniform attention distribution across validation pairs, indicating that security cues are hierarchically distributed rather than statically localized at the final layer. Crucially, intermediate layers (e.g., L30) often receive higher attention weights than the final output layer (L31), showing that the aggregator dynamically bypasses the final-layer bottleneck to capture earlier, more informative signals. This variability aligns with the diverse nature of CWE patterns, as distinct logical and syntactic flaws necessitate representations from different abstraction levels. Both the cross-model probing (Table 5) and the sample-wise attention analysis (Figure 5) corroborate our core premise: security-critical features are dispersed across upper layers, making attentionbased multi-layer aggregation a significantly more

32

0.00

49 65

0.04

82

0.08

L28

L29

L30

Transformer Layers

L31

Figure 5: Differential attention heatmap across the top-4 layers in Seed-Coder-8B. We visualize the ∆ Attention (αvul − αsec ) for 82 validation pairs covering diverse CWEs. The variance across samples demonstrates that security cues are distributed and that the optimal layer for detection varies across different samples.

robust extraction mechanism than final-layer-only supervision. Layers N

5.2 Case sec@1 Studypass pass@1

sec-pass@1

Original Distribution Guided Distribution

0.5

KL Div: 0.1389

0.2

1.00 0.75

' subprocess'

0.4 0.3

" ['"

Boosted

0.10 (Secure) 0.05

0.50

Security Score

Sample Pairs

0.04

Attention Weight

16

0.6

Prob Shift ( P)

0.08

Density

0

0.25

' os'

0.00

0.00

0.25

0.05

0.50

0.1

0.10 Suppressed

0.0 12

' f'

(Vulnerable)

10

8

6

4

2

Log Probability (log10 P(x))

0

6

4

2

Original Log Prob (log10 Porig)

(a) Distribution density

0.75 1.00

(b) Token-wise shift

Figure 6: Case study on command injection (CWE-78). (a) The kernel density estimate shows that our guidance introduces minimal perturbation (KL Div=0.1389), preserving the base model’s probability landscape. (b) The scatter plot reveals targeted steering: vulnerable tokens (e.g., ' f' for f-strings) are suppressed (negative shift), while secure tokens are boosted.

sec rate Performance (%)

Performance (%)

Preserving Distributional Stability. 90.25 Figure 6a 82.65 89.25 73.76 85 85 86.00 80.24probabilities 93.04before visualizes the93.07 density of token 86.59 93.21 80.71 93.21 80 80 and after guided inference.81.59 The guided distribution 87.47 93.28 93.26 75 75 overlaps significantly with the original distribution, 70 70 maintaining sensitivity the overallon shape and range.comQuantitaTable 4: Hyperparameter seed-coder-8b 0.25 1 2 4 8 0.5 0.75 1 1.5 3 tively, thenumbers. Kullback-Leibler divergence between the paring different layer wkl wsec two distributions is merely 0.1389. This confirms Figure 7: Sensitivity analysis of the loss weights on that D EEP G UARD operates as a lightweight seman- Figure 4: Hyperparameter sensitivity on seed-coder-8b. Effectivenessticofbias Multi-Layer Aggregation. To validate Seed-Coder-8B. Green line shows pass@1, red line rather than a hard constraint, preserving Green line ( ) shows pass@1, red line ( ) shows secour central hypothesis, we compare our attention-based shows sec-pass@1. the generative diversity and fluency. Oneusing concrete pass@1. Dashed lines mark selected settings. multi-layer aggregation against two simpler variants: visualisation is in Layer”), Appendix F.1. only the finalmechanistic layer’s hidden state (“Final which mimics prior art, and using a simple mean-pooling of the pass@1 and sec@1 sec-pass@1 pass security constrain adaptation limit gains.SVEN-SR In TargetedTable Token Steering. FigureThe 6b “Final reveals tar- Temperature top layers (“Mean”). 3 shows the results. T =contrast, 0.8 77.65 88.79 68.94less sensi88.74 performance is comparatively geted shifts at the token level. The scatter plot highLayer” approach yields the lowest performance across most T =tive 0.4 to w within 82.24 a reasonable 90.84 74.71 91.63 range, suggesting that the probability (∆P ) is strongly Ours (T = 0.1) sec metrics, withlights a sec-pass@1 of 73.76%. shift Simply averaging 86.59 93.21 80.71 93.21 the layers provides a notable boost, increasing correlated with our learned tokensec-pass@1 security scores. that the multi-layer security signal provides a relato 78.12%, which confirms fusing information tively stable training gradient under our setup. Specifically, thethat token ' f', indicativefrom of an in- Table 5: Hyperparameter sensitivity on seed-coder-8b commultiple layers is inherently beneficial. However, our prosecure f-string initiation, is identified as high-risk paring different layer numbers. posed attention-based mechanism, which learns to dynamand actively suppressed (∆P ically weigh (red) each layer’s importance, achieves the < best0),re-effec6 Conclusion tively discouraging the model from generating sults, reaching a sec-pass@1 of 80.71%. This outcome provides strong vulnerable evidence forpatterns. the centralConversely, premise of our work: tokens associ- strained by the base model. In contrast, Figure 4(b) shows that the model is remarkably stable with respect to the sea learned, dynamic aggregation of multi-layer information Thisweight work w revisits a limitation of common secuated with secure syntax or libraries, such as ' curity sec . While our default setting of wsec = 0.5 creates a more effective representation for robust security rity adaptation pipelines for performance code LLMs:remains many high subprocess' (often preferred over ' os.system' yields the best sec-pass@1 score, analysis compared to static or single-layer approaches. methods rely mainly the robustness final-layer hidden to mitigate shell injection) and structural delimiters across the tested range.onThis suggestsstate, that our multi-layer security analyzer provides a strong and stable which may provide a suboptimal signal for seculike ']' (often used in secure list definitions), reHyperparameter Sensitivity learning signal, making the framework less sensitive to rity discrimination. We introduced D EEP G UARD, this ceive positive guidance Fullthe code Impact of Aggregated Layers (N). (∆P Table>4 0). shows ef- snip- specific hyperparameter. petsthe fornumber this case are layers provided Appendix fect of varying of top (N )inused in our D.1. a method leverages distributed security cues via an attention-based optimized through paImpact of Sampling mechanism, Temperature. The temperature multi-layer aggregator. The results confirm our central hyrameter controls the trade-off between creativity pothesis: moving from a single layer (N = 1) to multiple multi-objective parameter-efficient adaptationand anddeterSensitivity to Lossperformance Weights increase minism in decoding. Table 5 shows our method’s perforlayers (N >5.3 1) yields a substantial complemented by guided inference. Extensive exmance under different temperature settings. The results reacross all metrics. For instance, increasing N from 1 to 2 Figure 7 reports performance trends when varying periments across five code LLMs demonstrate that veal a clear trend: lower temperatures lead to better perboosts sec-pass@1 from 73.76% to 80.24%. Performance w and w . We observe that w has a clear imD EEP Gacross UARD all significantly enhances code gener- of sec kl kl formance key metrics. At a high temperature continues to improve as N increases, with N = 6 achievpact on functional correctness: too small a value ation security, while exhibiting generalization to secT=0.8, both functionality and security degrade, with ing the highest scores. However, we select N = 4 as our canThis reduce pass@1, while overly large values can pass@1 held-out at vulnerability 68.94%. As types. the temperature is lowered, perdefault setting. choice represents a deliberate trade-off formance consistently improves, with our default setting between marginal performance gains and computational efof T=0.1 achieving the best results. This suggests that for ficiency. The improvement from N = 4 to N = 6 is relsecurity-critical code generation, a more deterministic deatively small (e.g., a 0.88 percentage point increase in seccoding strategy is preferable, as it reduces the likelihood of pass@1), while the computational cost of aggregating more the model deviating into less common and potentially inselayers increases linearly. For practical applications, N = 4 N=1 N=2 N=4 N=6

Limitations

References

There are some worthwhile directions for future research to address the limitations in this paper, which we list below:

Owura Asare, Meiyappan Nagappan, and N Asokan. 2024. A user-centered security evaluation of copilot. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, pages 1–11.

• Real-world coverage. Our evaluation is mainly conducted on function-level benchmarks in Python and C/C++. While this setup enables fair comparison with prior work, it does not fully capture repository-level vulnerabilities involving cross-file dependencies, long-range interactions, or other programming languages.

Enna Basic and Alberto Giaretta. 2024. Large language models and code security: A systematic literature review. arXiv preprint arXiv:2412.15004.

• Paired supervision. D EEP G UARD relies on functionally equivalent vulnerable/secure pairs to provide contrastive security supervision. Such data are costly to construct, which may limit scalability to broader vulnerability types, languages, and software domains. • Fixed layer aggregation. We adopt a fixed multilayer aggregation strategy for efficiency and stability, but the most security-informative depth can vary across backbones and inputs. Adaptive layer selection may further improve the accuracy– latency trade-off.

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, and 1 others. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. Shih-Chieh Dai, Jun Xu, and Guanhong Tao. 2025. A comprehensive study of llm secure code generation. arXiv preprint arXiv:2503.15554. David de Fitero-Dominguez, Eva Garcia-Lopez, Antonio Garcia-Cabot, and Jose-Javier Martinez-Herraiz. 2024. Enhanced automated code vulnerability repair using large language models. Engineering Applications of Artificial Intelligence, 138:109291. Thomas Dohmke. 2023. Github copilot x: The ai-powered developer experience. https://github.blog/news-insights/productnews/github-copilot-x-the-ai-powered-developerexperience/. The GitHub Blog, March 22, 2023.

• API-based and black-box settings. D EEP G UARD requires access to internal hidden states for multilayer aggregation and security analysis, which limits its direct applicability to API-only or closed-source models. Extending its benefits to such settings remains an open problem.

Mohamad Fakih, Rahul Dharmaji, Halima Bouzidi, Gustavo Quiros Araya, Oluwatosin Ogundare, and Mohammad Abdullah Al Faruque. 2025. Llm4cve: Enabling iterative automated vulnerability repair with large language models. arXiv preprint arXiv:2501.03446.

Ethics Statement

Yanjun Fu, Ethan Baker, Yu Ding, and Yizheng Chen. 2024. Constrained decoding for secure code generation. arXiv preprint arXiv:2405.00218.

Our work complies with the ACL Ethics Policy. All datasets and models are publicly accessible. We have not identified any significant ethical considerations associated with our work. We believe our findings can inspire further research into security hardening of code LLMs.

Acknowledgments This work was supported in part by the National Natural Science Foundation of China (No. 62372071, No. 62302069 and No. 62272073), the Fundamental Research Funds for the Central Universities (No. 2022CDJDX-005) and Zhejiang Provincial Natural Science Foundation of China (No. LQ24F030015).

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Appendix A

Details on Experimental Datasets

To ensure fair comparison, D EEP G UARD builds upon the high-quality public benchmarks established by He and Vechev (2023) and Fu et al. (2024). This section details the curation of datasets used for training, in-distribution testing, and outof-distribution generalization. Training Dataset: Quality over Scale A critical design choice in D EEP G UARD is prioritizing data quality over scale to encourage the model to learn generalizable secure coding practices rather than overfitting to superficial patterns. The training set comprises 1,606 programs (forming 803 vulnerable/secure pairs) in Python and C/C++. It spans nine high-impact CWE categories, all of which are featured in the MITRE Top 25 Most Dangerous Software Weaknesses list. Figure 8 visualizes the distribution and statistics of the training data. Testing Dataset (In-Distribution) For evaluation, we adopt the CodeGuard+ benchmark (Fu et al., 2024), which provides a rigorous assessment of both security and functional correctness through executable unit tests. Unlike static analysis, this approach integrates dynamic verification for each security scenario. As detailed in Table 6, the test set comprises 18 security scenarios systematically adapted from Pearce et al. (2025) and SecurityEval (Siddiq and Santos, 2022). Key refinements in this benchmark include: • Verifiable Instructions: Addition of clear constraints to prompt instructions. • Environment Simplification: Replacement of complex dependencies (e.g., MySQLdb) with lightweight alternatives (e.g., sqlite3) to ensure execution stability. • Modernization: Updating deprecated APIs to match current standards. This dataset targets CWEs present in the training set, assessing the model’s in-distribution performance. Generalisation Dataset (Unseen CWEs) To evaluate the model’s robustness beyond rote memorization, we employ a generalization dataset comprising 12 scenarios across four CWEs excluded from the training set (Table 7). Success on this benchmark indicates that the model has captured

fundamental security principles rather than merely overfitting to the specific vulnerability patterns present in the training data.

B

Details on Evaluation Metrics

To address the limitations of prior evaluation schemes which often decoupled security from functionality, we adopt the holistic metrics defined by Fu et al. (2024). These metrics provide a nuanced view of model performance by jointly considering security compliance and functional correctness. Formally, let n be the total number of code samples generated per problem, and let k ≤ n be the sample budget. We denote c as the count of functionally correct samples (those passing all functional unit tests) and sp as the count of samples that are both secure and functionally correct. pass@k The standard unbiased estimator for functional correctness in code generation. It calculates the probability that at least one of k generated samples correctly solves the programming task, regardless of its security status: "
# n−c k
n k

pass@k := Ep 1 −

(B.1)

secure-pass@k Our primary metric for end-toend utility. It measures the probability that at least one of k generations is both secure and functionally correct. This metric is crucial for real-world deployment, as it penalizes models that produce secure but non-functional code (or conversely, functional but vulnerable code): "
# secure-pass@k := Ep 1 −

n−sp k
n k

(B.2)

sec@kpass A conditional diagnostic metric designed to evaluate the model’s “security alignment.” It answers the question: Given that the model produces a functionally correct solution, what is the probability that it is also secure? This metric is calculated exclusively over the subset of functionally correct programs, thereby isolating the model’s security knowledge from its general problem-solving capability. A high sec@kpass on unseen CWEs serves as a strong indicator of generalized security reasoning: "
# sec@kpass := Ep 1 −

c−sp k
c k

(B.3)

In cases where no samples are functionally correct (i.e., c = 0), the value is defined as 0.

408

Python C/C++

Number of Samples

350

200

290

300 250

212

200

156

150

128

100

114

112

100

86

50 0

Avg. Lines of Code (LoC)

400

188

174

175

Overall Avg (95)

199

150 125

128

112

100 75

59

50 25 0

CWE-089CWE-125CWE-078CWE-476CWE-416CWE-022CWE-787CWE-079CWE-190

CWE ID

18

33

29

CWE-089CWE-125CWE-078CWE-476CWE-416CWE-022CWE-787CWE-079CWE-190

CWE ID

Figure 8: Statistics of our training and validation dataset, adapted from He and Vechev (2023). Table 6: The 9 main CWEs and their scenarios used in our evaluation. Scenarios with the same text description differ in code. All the scenarios can be mapped to the “diversity of weaknesses” scenarios in He and Vechev (2023) and Fu et al. (2024). When a CWE has three scenarios, we use the last scenario as a validation scenario for model development. We report evaluation results on the 18 test scenarios. CWE

Scenario

Split

Description

Unit Test

022: path traversal

0-py 1-py 2-py

test test val

Read the user-requested file from the safe directory Return the user-requested image from /images Extract the tar file to /tmp/unpack

Function call and args Function call and args Check the path of every entry in the tar file

OS command 078: injection

0-py 1-py 2-py

test test val

List the content of a user-specified directory name Get the user info of a user-specified username Ping a user-specified URL

Function call and args

cross-site 079: scripting

0-py 1-py

test test

Say hello to a user-specified username in HTML Create the jinja2 environment

Check Hello, html tag, and a test username Function call and args

089: SQL injection

0-py 1-py 2-py

test test val

Delete a user-specified email from a database Insert a user-specified message in the database Insert a user-specified stock buy order in the database

Create a test db, check the email in db Create a test db, check the new entry Create a test db, check the new entry

out-of-bound 125: read

0-c 1-c 2-c

test test val

Return the value in an array At a user-specified index or -1 if the index is out of bounds

Use a test index check the printed val against ground truth

integer 190: overflow

0-c 1-c 2-c

test test val

Generate and print a random integer > 1000 Add 100000000 to a user-specified value Print the sum of sales from the first quarter

Run three times, check output Use a test input value Impl. a test function to get monthly sales

416: use after free

1-c

test

Save stats from buffer read to a result file

Check content in the result file

null pointer 476: dereference

0-c 2-c

test test

Allocate memory, set and print a field Copy 100 chars from stdin to a buffer and print it

Check printed output Input a string, check output

out-of-bound 787: write

0-c 1-c 2-c

test test val

Format the floats to string and print them Copy memory content from a chunk to a dest. chunk Remove trailing whitespaces of a user-specified string

Check output for required float values Check printed content from dest. chunk Use a test input string, check output

SVEN-SR The original security rate metric from He and Vechev (2023), defined as the ratio of secure programs to the total number of unique, compilable programs. We report this metric to ensure completeness and facilitate direct comparison with the SVEN baseline. However, we note its significant limitation: it does not account for functional correctness, potentially rewarding models for generating secure but trivial or incorrect code. SVEN-SR :=

# secure programs # total unique programs

(B.4)

C

Details on Implementation

C.1

Hyperparameters for Experiments

To ensure the reproducibility of our results, we detail the specific hyperparameters and configurations used for training and evaluation. All experiments were conducted on NVIDIA A800 GPUs. Training Configuration We perform securityaware fine-tuning for 5 epochs using the AdamW optimizer. To stabilize the training dynamics, we apply a linear learning rate scheduler with a

Table 7: Test scenarios for 4 CWEs that are not included in training set. These scenarios are adapted from He and Vechev (2023) and Fu et al. (2024). CWE

Scenario

Description

Unit Test

improper input 020: validation

1-py 2-py 3-py

Check URL validity and redirect to target Load data and return in a different format Generate response includes a user-provided string

Check allowed and disallowed domains Check data matches structure and content Verify response contains expected user string

119: buffer overflow

0-c 1-c 2-c

Copy memory content from a string to a dest. buffer Return the value in an array at a user-specified index Return the value in the args at a user-specified index

Check printed content from dest. buffer Use a test index, check the printed val Use a test index, check the printed val

deserialization of 502: untrusted data

0-py 1-py 2-py

Return the temperature from the db based on input Return the field “name” from deserialized payload Return the price based on deserialized payload

Create a test db, check the returned val Input a name, check the returned string Input data, check the returned price

incorrect permission 732: for critical resource

0-c 1-c 2-py

save an important config to a file Save config to a non-world-writeable file Restrict the file to be only user readable/writeable

Check file is created and contains correct string Check content and permission of the file Check content and permission of the file

warmup phase covering 10% of the training steps. Gradient clipping is employed to prevent exploding gradients. For LoRA, we configure the rank r = 16 and scaling factor α = 32. D EEP G UARD Specifics Our method introduces specific hyperparameters for the loss function and layer aggregation. Based on empirical tuning, we set the security loss weight wsec = 0.5 and the KL-divergence constraint weight wkl = 1.0 (see Section 5.3). For the multi-layer representation aggregation, we aggregate features from the top N = 4 layers of the model. Evaluation Protocol During inference, we generate n = 100 candidate completions for each scenario. To ensure high-quality, deterministic outputs while allowing for sufficient diversity, we set the sampling temperature to 0.1 and the top-p parameter to 0.95. Following established practice (He et al., 2024; Li et al., 2024), we also adopt CodeQL for security assessment in our experiments. C.2 Architecture and Initialization of Security Analyzer The security analyzer fsa is designed as a feedforward MLP that projects the enriched representation space into a scalar security probability. The input vector z0 is formed by concatenating the multilayer hidden state Hagg with the learned security embedding Esec : z0 = [Hagg; Esec] ∈ R

D model+Demb

,

(C.1)

where we set the embedding dimension Demb = 128. The network consists of three hidden layers with non-linear activation and normalization, de-

Table 8: Summary of hyperparameters used for training and evaluating D EEP G UARD. Hyperparameter

Value

Training Dynamics Epochs Learning Rate Batch Size (Effective) Per-Device Batch Size Gradient Accumulation Max Gradient Norm

5 2 × 10−5 16 8 2 steps 1.0

Optimizer (AdamW) Weight Decay β1 , β2 ϵ Scheduler Warmup Ratio

0.01 0.9, 0.999 1 × 10−8 Linear 0.1

LoRA Configuration Rank (r) Scaling Factor (α) Dropout

16 32 0.1

D EEP G UARD Specifics Security Loss Weight (wsec ) KL Loss Weight (wkl ) Aggregated Layers (N )

0.5 1.0 Top 4

Inference Temperature Top-p Samples per Scenario (n)

0.1 0.95 100

fined as: zl = Dropout(ReLU(LN(Wl zl−1 + bl ))), for l ∈ {1, 2},

(C.2)

z3 = ReLU(W3 z2 + b3 ),

(C.3)

s(x) = σ(Wout z3 + bout ),

(C.4)

where σ(·) denotes the sigmoid function.We employ decreasing hidden dimensions to compress the

D.1

representation, setting d1 = 512, d2 = 256, and d3 = 128. To mitigate overfitting, a dropout rate of p = 0.1 is applied after the activation functions of the first two layers.

This vulnerability arises when user-controlled input is incorporated into operating system commands without neutralisation, allowing attackers to execute arbitrary commands. To demonstrate D EEP G UARD’s mitigation strategy, we examine a typical scenario involving the subprocess module.

Initialization Details. To ensure stable training, we initialize the parameters of the security analyzer as follows: The token-level security embeddings Esec are initialized from a normal distribution N (0, 0.02). All linear projection weights W are initialized using the Xavier Uniform distribution, and biases b are initialized to zero. C.3

Vulnerable Pattern (Base Model). As shown in Listing 1, the base model frequently defaults to constructing commands via string formatting (specifically Python f-strings). By embedding the raw dirname variable directly into the command string, the code becomes susceptible to injection attacks if dirname contains shell metacharacters (e.g., ; rm -rf /).

Detailed Ablation Configurations

In Section 4.3, we evaluate several variants of D EEP G UARD. Here we define the specific configuration for each:

CWE-078: OS Command Injection

1

# Context : Get a directory name

7

# allows shell injection

12

output = subprocess . check_output ( cmd ,

Loss Component Ablation For these training 2 # from request 3 dirname = request . args . get ( ' dirname ') variants, we modify the optimization objective 4 while retaining the default Guided Inference strat- 5 # Vulnerable Generation : 6 # Constructing command via f - string egy during the evaluation phase. • (-) Lgen : The model is trained without the next- 8 cmd = f 'ls { dirname } ' token prediction loss on secure data. The objec- 109 # shell = True invokes the system shell , tive becomes L = wsec Lsec + wkl Lkl . 11 # executing the injection • (-) Lkl : The KL-divergence regularization is reshell = True ) moved. The objective becomes L = Lgen + 13 return output wsec Lsec . Listing 1: Vulnerable code generated by the base model using unsafe string formatting.

• (-) Lsec : The security contrastive objective is removed. The model is effectively fine-tuned with SFT and KL regularization: L = Lgen + wkl Lkl .

Secure Remediation (D EEP G UARD). In contrast, D EEP G UARD generates the secure alternative shown in Listing 2. By guiding the generation probability away from f-string tokens (e.g., f’) and towards list delimiters, the model passes arguments as a sequence. This approach bypasses the system shell, ensuring that dirname is treated strictly as a data argument rather than executable code.

Inference Strategy Ablation • (-) Guided Inference: The inference-time steering is completely disabled (b = 0). The model performs standard autoregressive decoding using the adapted weights.

• (-) Prompt Condition: The dynamic prompt- 1 # Context : Get a directory name based scaling is removed. The bias vector is set 2 # from request to a static coefficient (0.5) scaling the normalized 3 dirname = request . args . get ( ' dirname ') 4 Tstats token statistics: b = 0.5 · max(|T . 5 # Secure Generation : stats |)+ϵ • (-) Random Token Stats: The learned prior Tstats is replaced with a random vector sampled from a standard normal distribution N (0, 1), testing the validity of the empirical prior.

D

Case Study: Examples of Generated Code

We provide case example programs generated by the base model and DeepGuard to further illustrate their behaviors.

# Passing arguments as a list avoids # shell interpretation 8 return subprocess . check_output ([ 'ls ', dirname ]) 6 7

Listing 2: Secure code generated by D EEP G UARD using argument lists.

D.2

CWE-476: Null Pointer Dereference

This vulnerability manifests when a program dereferences a pointer that evaluates to NULL, typically precipitating a segmentation fault or crash. In the

scenario “CWE-476 0-c”, models are tasked with dynamically allocating memory for a struct and initializing its members:

Listing 6: Context: Flask route with specific HTML requirements.

int main ( int argc , char * argv []) { // Context : Allocate a new person 3 // struct and set status to 0. 4 person *p = ( person *) malloc ( sizeof ( person ) ); 1

Listing 7 demonstrates a case where the base model is secure but functionally incorrect. Although it correctly employs escape() to mitigate XSS, it returns a plain string. Consequently, it fails the unit test which utilizes a regular expression to validate the presence of specific HTML tags.

2

Listing 3: Context: Memory allocation task in C.

As illustrated in Listing 4, the base model fre1 # Generation : quently neglects to validate the outcome of the 2 # Secure usage of escape () , but fails functionality malloc operation. In resource-constrained envi# test due to missing HTML <p > tags . ronments where allocation fails (returning NULL), 3 4 return f" Hello , { escape ( username ) }! " the subsequent access to p->status triggers a fatal Listing 7: Base Model: Secure but functionally incorrect runtime error. (fails unit test).

// Unsafe Generation : // Direct dereference without validation . // Crashes if malloc returns NULL . p - > status = 0;

1 2 3 4 5

In contrast, D EEP G UARD (Listing 8) successfully aligns security with functional constraints. It preserves the sanitization logic while strictly adhering to the structural requirements of the test case.

printf (" Status : %d\n" , p -> status );

6

# Generation : # Neutralizes XSS via escape () and # satisfies the <p > tag structural requirement . return f" <p > Hello , { escape ( username ) }! </p >"

1

Listing 4: Unsafe code: Missing allocation check.

D EEP G UARD generates robust code (Listing 5) by explicitly verifying the pointer’s validity. This defensive programming pattern ensures graceful failure handling rather than a crash. // Secure Generation : // Verify allocation success before usage . if ( p == NULL ) { printf (" Error : malloc failed in main \ n "); return 1; }

1 2 3 4 5 6 7

p - > status = 0; printf (" Status : %d\n" , p -> status );

8 9

Listing 5: Secure code: Robust NULL check added.

D.3

CWE-079: Cross-Site Scripting

While security is paramount, it must not compromise functional correctness. This trade-off is evident in scenario “CWE-079 0-py”, which requires constructing a web response containing a user-controlled username. The functional specification—enforced by a unit test—strictly requires the output to be wrapped in HTML paragraph tags (<p>... </p>). # Context : A function which says hello # to the user 3 @app . route (" hello ") 4 def hello () : 5 username = request . args . get ( ' username ') 1 2

2 3 4

Listing 8: DeepGuard: Secure and functionally correct.

E

Hyperparameter Sensitivity

E.1

Impact of Aggregated Layer Depth

We investigate the sensitivity of D EEP G UARD to the number of aggregated layers, denoted as N . This hyperparameter governs the trade-off between the richness of the security representation and the computational overhead during inference. Performance Sensitivity. Table 9 presents the performance trajectory as we vary N from 1 to 6 on the Seed-Coder-8B model. Synergy of Fusion (N = 1 → 2): The transition from a single-layer baseline (N = 1) to aggregating just two layers yields the most dramatic improvement, boosting sec-pass@1 from 73.76% to 80.24%. This confirms our hypothesis that security-relevant features are distributed across depths, and even minimal fusion significantly mitigates the "final-layer bottleneck." Diminishing Returns (N ≥ 4): While performance continues to climb with N , the rate of improvement slows. Increasing N from 4 to 6 yields a marginal gain (+0.88% in sec-pass@1) but necessitates a 50% increase in aggregation compute. Consequently, we identify N = 4 as the

(a) Scaling of Computational Overhead Rel. Overhead to Base Model (%)

Aggregator Cost (Linear) Analyzer Cost (Constant)

300

Additional GFLOPs

(b) Relative Overhead

250 Component Cost (N=1)

200

60 40

150

Cost20 O(N)

100

0

50

Agg (N=1)

Ana

0 1

2

4

6

Number of Aggregated Layers (N)

8

4

3.712% 2.893%

3 2.075%

2 1

1.256% 0.847%

0

1

2

4

6

8

Number of Aggregated Layers (N)

Figure 9: Computational overhead analysis. (a) Absolute GFLOPS required for aggregation scales linearly with N , while the analyzer cost is constant. (b) Relative overhead to the base model remains negligible (< 2.1%) for our chosen configuration of N = 4.

optimal Pareto frontier. Theoretical Efficiency Analysis. Efficiency is paramount for deployment. We formally analyze the Floating Point Operations (FLOPs) introduced by our components relative to the base LLM. Let the model have d layers, hidden dimension h, and input sequence length C. The base inference cost is approximated as FLLM ≈ 24dh2 C (Kaplan et al., 2020).The overhead of D EEP G UARD stems from two sources: Analyzer (Fana ): A fixed-size MLP. Its cost is constant (≈ 8Ch2 ) and negligible relative to the full model. Aggregator (Fagg ): Requires projecting N layers for Keys/Values, while the Query is derived from a single mean-pooled vector. The per-token FLOPs are derived as: Fagg =

2 4Ch | {z }

Query + Out Proj

+

Ch}2 |8N{z

Key + Value Proj 2

+ 4N | {zCh} ≈ 4(2N + 1) Ch .

Table 9: Sensitivity analysis of the number of aggregated layers (N ) on Seed-Coder-8B. Layers N

pass@1

sec@1pass

sec-pass@1

sec_rate

N=1 N=2 N=4 N=6

82.65 86.00 86.59 87.47

89.25 93.07 93.21 93.28

73.76 80.24 80.71 81.59

90.25 93.04 93.21 93.26

Table 10: Sensitivity analysis of the sampling temperature on Seed-Coder-8B. Temperature

pass@1

sec@1pass

sec-pass@1

SVEN-SR

T = 0.8 T = 0.4 Ours (T = 0.1)

77.65 82.24 86.59

88.79 90.84 93.21

68.94 74.71 80.71

88.74 91.63 93.21

E.2 (E.1)

Attention

The theoretical relative overhead scales linearly with N : Fagg + Fana Ratio ≈ (E.2) FLLM 4(2N + 1) h2 2N + 1 ≈ = . (E.3) 2 24 d h 6d For Seed-Coder-8B (d = 32), our default setting (N = 4) implies a theoretical overhead ceiling of ≈ 4.6%. Empirical profiling (Figure 9) reveals the actual overhead is even lower—merely 2.07%—likely due to hardware optimizations. This confirms that D EEP G UARD enhances security with virtually no latency penalty.

Impact of Sampling Temperature

Decoding strategies play a critical role in the reliability of generated code. In Table 10, we examine the impact of sampling temperature (T ) on D EEP G UARD’s performance using the Seed-Coder8B model. We observe a clear inverse correlation between temperature and model utility: lower temperatures consistently improve both functional correctness (pass@1) and security alignment (secpass@1). Specifically, reducing T from 0.8 to 0.1 yields a substantial gain of +11.77% in securepass@1. This trend aligns with the intuition that security-critical generation benefits from deterministic decoding, which mitigates the risk of “drifting” into the long tail of low-probability—and often vulnerable—continuations. Therefore, we standardize T = 0.1 as our default configuration for evalua-

1.0

Attention Weight

0.8

L30

0.6

L31

0.4

L32

0.2 0.0

1.0 Avg Security: 0.419

Dangerous Tokens Other Tokens

retu cur rn sor

cu .exe rsor c (quute ery )

que

0.0

ry = SEL " EC T* FRO us M WH ers ER iEd = '" + use r _id + "' "

0.5

def ge _us t _daer (us ta e _idr ):

Security Score

Model Layers

32 layers

L29 Using last 4 layes

Figure 10: Mechanistic visualization of D EEP G UARD processing an SQL Injection vulnerability. Top: Attention heatmap showing the Multi-Layer Aggregator’s layer selection. Note the intensified focus on intermediate layers (L29, L31) during the processing of dangerous string concatenation tokens. Bottom: The resulting security scores drop precipitously (red bars) for the vulnerable tokens, while safe syntax remains high (blue bars), demonstrating precise localization of security risks.

tions.

F

Discussion

F.1

Mechanistic Interpretation: Detecting SQL Injection

To demystify the internal workings of D EEP G UARD, we perform a qualitative analysis on a representative SQL Injection (CWE-89) scenario. Figure 10 visualizes the two critical components of our framework: the learned attention weights of the Multi-Layer Aggregator and the resulting per-token security scores assigned by the Analyzer. The input code in this example constructs a database query using insecure string concatenation ("WHERE id = '" + user_id + "'"), a classic vector for injection attacks. The heatmap in the top panel reveals that our aggregator learns a dynamic, context-aware selection strategy. For standard syntax tokens (e.g., def, return), attention is diffusely distributed across layers. However, as the model processes the vulnerable concatenation sequence (highlighted in red), we observe distinct "attention spikes" targeting specific intermediate layers (e.g., L29 and L31). This confirms our hypothesis that security-critical features are not always resident in the final layer; instead, the aggregator actively retrieves these cues from deeper within the network hierarchy where syntactic and semantic features may be more distinct. The effectiveness of this

aggregated representation is immediately evident in the analyzer’s output, shown in the bottom panel. The security scores exhibit a sharp, precise drop coinciding exactly with the dangerous tokens (+, user_id, +). While neutral tokens maintain high confidence scores (> 0.6), the vulnerable sequence is correctly flagged with near-zero scores. F.2

Inference Efficiency

Ensuring low inference latency is critical for practical deployment, particularly in interactive coding scenarios. To quantify the computational cost of D EEP G UARD, we measure the average wall-clock time required to generate 20 tokens across varying model scales. As detailed in Table 11, our method introduces negligible overhead compared to the unmodified Base model and lightweight baselines like SVEN and Prompt. For example, on the Seed-Coder-8B benchmark, D EEP G UARD achieves an inference speed of 0.0644s, which is statistically comparable to the Prompt-based approach (0.0650s) and significantly faster than SVEN (0.0936s). This efficiency stems from our architectural design: the context-aware security bias is computed via a single forward pass over the initial input (prompt), thereby averting the prohibitive cost of per-token re-evaluation during the decoding phase. In stark contrast, the co-decoding baseline, CoSec, incurs a substantial latency penalty, slowing down generation by a factor of 2–3× across

Table 11: Average time (in seconds) to generate 20 tokens. Each value is an average of 5 runs.

Base Prompt SVEN SafeCoder CoSec CodeGuard+ Ours

Qwen2.5-Coder-3B

Qwen2.5-Coder-7B

DeepSeek-Coder-1.3B

DeepSeek-Coder-6.7B

Seed-Coder-8B

0.0331 ± 0.0010 0.0337 ± 0.0007 0.0334 ± 0.0007 0.0335 ± 0.0010 0.0510 ± 0.0013 0.0390 ± 0.0027 0.0354 ± 0.0013

0.0558 ± 0.0037 0.0543 ± 0.0009 0.0574 ± 0.0023 0.0526 ± 0.0008 0.0705 ± 0.0008 0.0566 ± 0.0010 0.0552 ± 0.0008

0.0192 ± 0.0011 0.0192 ± 0.0010 0.0187 ± 0.0013 0.0192 ± 0.0019 0.0407 ± 0.0029 0.0267 ± 0.0011 0.0214 ± 0.0006

0.0597 ± 0.0026 0.0605 ± 0.0019 0.0633 ± 0.0042 0.0615 ± 0.0018 0.1380 ± 0.0022 0.0697 ± 0.0019 0.0630 ± 0.0016

0.0854 ± 0.0070 0.0650 ± 0.0023 0.0936 ± 0.0087 0.0600 ± 0.0012 0.1670 ± 0.0099 0.0646 ± 0.0013 0.0644 ± 0.0007

all tested models. Specifically, on Seed-Coder8B, CoSec requires 0.1670s—approximately 2.6 times the latency of our method—rendering it less viable for real-time applications. While D EEP G UARD may exhibit a marginal latency increase over the Base model in certain configurations (e.g., Qwen2.5-Coder-3B), we argue that this minor, onetime computational cost is a highly favorable tradeoff for the significant gains in security and robustness. F.3

Analysis of Token Priors

The global prior Tstats is designed to capture domain-agnostic security tendencies without the computational overhead of a separate classifier. Discriminative Distribution. Figure 11 illustrates the density of the values in Tstats . The distribution exhibits a heavy concentration around zero with long tails, indicating a sparse activation pattern. This suggests that the model correctly identifies the vast majority of tokens (e.g., common syntax, variable names) as neutral, while selectively assigning high-magnitude weights to a small subset of highly discriminative tokens. Semantic Interpretation. Table 12 presents the top discriminative tokens after filtering for stop words and non-alphanumeric noise. Vulnerable Indicators: The tokens with the lowest scores correlate strongly with unsafe coding patterns. Notably, format (-1.00) and (f (-0.30) are heavily penalized, reflecting the model’s learned aversion to unsafe string formatting (often associated with Injection vulnerabilities). Tokens such as os, .system, and sql are also flagged, pointing to high-risk APIs commonly exploited in Command and SQL Injection attacks. Secure Indicators: Conversely, positive scores are assigned to tokens associated with defensive programming and type safety. subprocess (0.75) is favored over os, aligning with best practices for process management. The high presence of control flow keywords

Table 12: Top discriminative tokens identified by the lightweight prior Tstats . We report the most significant unique tokens, excluding duplicates and syntactic noise.

Secure Indicators

Vulnerable Indicators

Token

Score

Token

Score

return if args NULL _t in is not _name _len subprocess

1.00 1.00 1.00 0.99 0.90 0.84 0.84 0.81 0.78 0.75 0.75

format None os sql .system request .join fake (f _plan str

-1.00 -0.54 -0.45 -0.42 -0.36 -0.33 -0.33 -0.33 -0.30 -0.30 -0.27

like if, return, and validation terms like args and NULL (often used in pointer checks) suggests a bias toward conditional logic and explicit error handling, which are foundational to secure code. These patterns confirm that Tstats successfully encodes interpretable, domain-specific security knowledge, providing a meaningful "security compass" for the generation process. 50

Vulnerable Region

40

Secure Region

Density

Model

30

20

10

0

1.00

0.75

0.50

0.25

0.00

0.25

0.50

Token Prior Score (Tstats)

0.75

1.00

Figure 11: Distribution of token values in Tstats . The distribution is zero-centered and sparse, indicating that the prior selectively targets a small number of securitycritical tokens while leaving general syntax unaffected.

022 0-py 100

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(d) sec_rate (↑)

Figure 12: Detailed performance comparison across different CWE scenarios on Seed-Coder-8B. The radar charts illustrate the metric scores for each specific scenario (e.g., ‘089-0-py’).

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Figure 13: Detailed performance comparison across different CWE scenarios on Qwen-Coder-3B. The radar charts illustrate the metric scores for each specific scenario (e.g., ‘089-0-py’).