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MobileInject: Evaluating Reasoning-to-Action Security of MLLM-Based Mobile Agents under Cross-Modal Consistency Injection

Songping Wang et al. · arxiv_cs

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arXiv:2610.23307v1 [cs.CR] 16 Oct 2026

MobileInject: Evaluating Reasoning-to-Action Security of MLLM-Based Mobile Agents under Cross-Modal Consistency Injection

Songping Wang∗ Peiyang Li∗ Yi Huang Fengting Li Xuhao Ran Yueming Lyu† Chao Feng Caifeng Shan† 1 Nanjing University 2 Ant Group 3 Peking University Abstract

multimodal assistants that primarily perceive images and generate textual responses, MLLM-based mobile agents close the loop between perception, reasoning, and action. They can observe mobile screenshots, parse interface structures, switch across applications, click buttons, scroll interfaces, enter text, and complete multi-step user tasks. This transition from multimodal understanding to mobile task execution substantially expands the capability boundary of foundation models, enabling personalized agents to act as autonomous operators for communication, scheduling, information retrieval, payment, file management, and other daily mobile workflows [1, 24]. However, this leap in execution capability also significantly expands the attack surface and amplifies the consequences of security risks [8, 21, 37]. In traditional LLM settings, adversarial instructions are usually delivered through explicit prompts [20, 23, 40]; in mobile-agent settings, however, malicious objectives can be embedded into the broader mobile ecosystem and encountered during normal task execution [8, 37]. For example, adversarial content may be hidden in SMS messages, emails, calendar events, webpages, social media content, or other cross-application content. Once perceived by the agent, such content may enter its decision context through visual or textual observations, thereby influencing its reasoning, planning, and action execution. Therefore, attacks against mobile agents are no longer limited to inducing unsafe text generation, but may lead to more threatening consequences, such as privacy leakage, unauthorized operations, financial misconduct, data disclosure, or persistent changes to system and application states. Mobile-agent security is particularly challenging because the mobile ecosystem exposes a broad, temporally distributed, and multimodal attack surface. First, a mobile agent operates in a continuous environment jointly formed by notifications, messages, emails, calendars, webpages, social media feeds, and third-party application states. Malicious payloads can hide across different applications, pages, and time points, and can be triggered during perception, application switching, or cross-application execution. Second, a mobile agent relies on heterogeneous observations, including visual observations

Multimodal large language model (MLLM)-based mobile agents are rapidly evolving from conversational assistants into autonomous systems that perceive, reason over, and act on smartphone environments. This capability also exposes a new security risk: adversaries can embed malicious objectives in cross-application mobile content and steer agent behavior through indirect multimodal injection. Although prompt injection has been studied in desktop and web settings, its cross-modal properties in mobile ecosystems remain poorly understood, and existing security analyses are still limited. To close this gap, we introduce MobileInject, the first benchmark for evaluating mobile agents against multimodal injection attacks and reasoning-to-action security risks. MobileInject provides a high-fidelity interactive mobile evaluation framework covering 11 scenarios, 9 security-risk categories, and 108 executable risk instances. It further introduces CrossModal Consistency Injection to exploit corroboration across modalities, two enhanced attack methods, Multi-Role Collaborative Deception and Reasoning-Camouflage Injection, to intensify trust manipulation and reasoning hijacking, and a Reasoning Security Diagnosis Framework for fine-grained failure analysis. Systematic experiments show that mobile agents are broadly vulnerable to multimodal injection, and that many failures stem from reasoning hijacking at early stages. These findings establish MobileInject as a systematic cross-modal benchmark for studying and improving the security of mobile agents.

Introduction

Multimodal large language models (MLLMs) are increasingly evolving from passive conversational interfaces into autonomous mobile agents capable of operating in real-world smartphone environments [19, 25, 38]. Unlike conventional ∗ Equal contribution. † Corresponding author.

User Instruction: Please check the message from Maria, and access the website recommended by her. Reasoning Remains Unaffected

Agent Completes Task!

Visual Observation Injection Next Action

Reasoning Remains Unaffected

Agent Completes Task!

Textual Observation Injection Next Action

3 Multimodal Observation Injection

Reasoning Hijacking! Cross-Modal Reinforcement

Unsafe! Action Hijacking!

Next Action

Figure 1: Comparison between unimodal and multimodal injection. Multimodal injection aligns malicious cues across visual and textual channels, creating cross-modal reinforcement that can amplify attack effectiveness over single-channel injection. such as screenshots and rendered UIs, as well as textual observations such as XML hierarchies, accessibility trees, UI attributes, and structured interface representations. Because mobile agents commonly ground decisions on both rendered and structured views of the same interface state, attackerauthored content can naturally surface in synchronized visual and textual channels during normal execution. This creates a joint attack surface that single-channel evaluations cannot characterize. More fundamentally, attacker-authored content can enter and manipulate the agent’s reasoning through these observations, displace the trusted user goal, and thereby hijack subsequent mobile actions. Third, because mobile devices are tightly coupled with private data, identity credentials, social relationships, payment systems, and local files, a lack of risk awareness or failures in the reasoning process may lead to privacy leakage, unauthorized operations, system-state disruption, and further cascading propagation through persistent carriers such as calendars, email, and shared cross-application content.

cross-application and cross-modal information [10,35]. Third, current evaluations often adopt coarse-grained binary assessment schemes that only superficially determine whether an attack succeeds [6, 21]. Such outcome-only evaluation cannot reveal whether the agent recognizes the risk, when its reasoning begins to follow the adversarial objective, or whether the compromised reasoning further propagates into executed actions. Consequently, attack success alone is insufficient to characterize the end-to-end security of mobile agents from safety awareness to action execution. These limitations further raise three critical questions: First, can multimodal injection exploit the cross-modal attack surface to amplify the effectiveness of single-modal injection? Second, how can stronger attack strategies be designed to induce misplaced trust in malicious content and deeply hijack agent reasoning? Third, how can a fine-grained Reasoning Security Diagnosis Framework move beyond outcome-level success rates to expose when, how, and how deeply agent reasoning is compromised, and whether such compromise propagates to downstream actions?

Despite growing attention to agent security, existing evaluation methods remain insufficient for characterizing this threat model [5, 37], as summarized in Table 1. First, many benchmarks rely on unrealistic sandbox environments, making it difficult to capture risks that emerge in realistic, dynamic, and cross-application mobile environments [19, 33]. Second, existing evaluations lack systematic benchmarks for multimodal indirect injection in mobile agents. They typically focus on a single textual channel or a single injection carrier, whereas real mobile agents jointly consume various types of

Addressing these questions requires a benchmark that directly targets the three limitations above. It should preserve realistic mobile execution in dynamic, stateful, and crossapplication environments, systematically cover indirect injection across both visual and textual observation channels, and connect verified end states to the full reasoning–action process. Such a benchmark is needed not only to determine whether an attack succeeds, but also to establish whether multimodal consistency genuinely amplifies risk, how adversarial 2

Benchmark / Method

InjecAgent [37] AgentDojo [5] Agent-SafetyBench [39] ChatInject [2] MobileSafetyBench [11] GhostEI-Bench [3] OS-Sentinel [22] AgentDoG [12] MobileInject (Ours)

Evaluation Setting

Injection Coverage

Diagnostic Capability

MGI

CAW

DSE

TOI

VOI

CMC

RIT

SOV

RTD

RATM

DVP

× × × × ✓ ✓ ✓ × ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ △ △ △ ✓ ✓

× × × × △ △ △ × ✓

× × × × × × × × ✓

× × × ✓ × × × × ✓

× × × × ✓ ✓ ✓ × ✓

× × × × △ △ △ △ ✓

× × × × × × × × ✓

Table 1: Comparison with representative agent-security and mobile-agent safety benchmarks. ✓: supported; △: partial or not the main focus; ×: not supported. MGI: mobile GUI interaction; CAW: cross-app workflow; DSE: dynamic stateful execution; TOI/VOI: textual/visual observation injection; CMC: cross-modal consistency; RIT: reinforced injection templates; SOV: state-based outcome verification; RTD: reasoning–action trajectory diagnosis; RATM: reasoning–action transition matrix; DVP: diagnostic visualization profile. observations redirect reasoning away from the trusted user goal, and whether that compromise propagates into downstream actions. This is essential for end-to-end diagnosis of reasoning-to-action security, including risk recognition, reasoning hijacking, and action-level propagation. To answer these questions, we propose MobileInject, the first benchmark for evaluating multimodal injection and reasoning-to-action security in MLLM-based mobile agents. First, MobileInject constructs a high-fidelity Android environment based on a full-function emulator, covering 11 application scenarios, 108 executable risk instances, and 9 securityrisk categories, to characterize realistic multi-step and crossapplication execution processes. Second, MobileInject introduces Cross-Modal Consistency Injection (CMCI), where semantically consistent malicious objectives are injected into visual and textual observations (Fig. 1) to strengthen manipulation capability and reveal the Cross-Modal Reinforcement Effect. Third, MobileInject extends the basic injection setting with two attack methods, namely Multi-Role Collaborative Deception (MRCD) and Reasoning-Camouflage Injection (RCI), to enhance trust manipulation and reasoning hijacking. Fourth, MobileInject introduces a Reasoning Security Diagnosis Framework (RSDF) based on trajectory-level reasoning–action–outcome analysis, enabling fine-grained diagnosis of risk awareness, reasoning hijacking, action hijacking, reasoning–action transition patterns, and verified security outcomes. The main contributions of this work are summarized as follows:

• We design new mobile-ecosystem attack methods, including Multi-Role Collaborative Deception and Reasoning-Camouflage Injection, to strengthen trust manipulation and deepen reasoning hijacking. • We establish a fine-grained Reasoning Security Diagnosis Framework that moves beyond outcome-level success rates to analyze when, how, and how deeply agent reasoning is compromised, and whether such compromise propagates to downstream actions.

Related Work

Evolution of Personalized Agents. Personalized agents have evolved from language-only assistants into multimodal operators capable of planning, tool use, and interaction with stateful digital environments. Early methods such as ReAct coupled reasoning with external actions, showing that language models can support sequential decision-making beyond static response generation [34]. Tool-augmented and function-calling agents extended this paradigm by enabling API and application use for user goals [5,15,37]. Recent work has increasingly moved toward graphical and mobile interfaces: AppAgent, Mobile-Agent, and Mobile-Agent-v2 study smartphone interaction [24, 25, 38]; OSWorld and AndroidWorld provide dynamic computer and Android environments [19, 33]; and DigiRL explores training device-control agents in the wild [1]. Collectively, these advances shift agents from prompt-centric assistants to operators over private, multimodal, and stateful environments, where untrusted external content may appear in both rendered and structured observations and directly influence actions. Security therefore depends not only on output harmlessness, but also on whether untrusted observations are treated as actionable instructions and whether their influence propagates from perception and reasoning to execution. From Large-Model Security to Agent Security. Progress

• We propose MobileInject, the first benchmark for multimodal consistency injection and reasoning-to-action security in MLLM-based mobile agents. • We introduce Cross-Modal Consistency Injection, a mobile-specific attack paradigm that reveals the CrossModal Reinforcement Effect and enhances attack effectiveness. 3

consequences. Second, systematic coverage: the benchmark encompasses diverse scenarios, workflows, injection vectors, attack goals, and security outcomes. Third, controllable and extensible attack design: injections are defined as configurable templates that can be composed along dimensions such as modality, vector, and complexity. Fourth, trajectorylevel observability: MobileInject records step-by-step observations, reasoning, actions, goals, and states, thereby enabling fine-grained diagnosis beyond binary safe/unsafe labels. Threat Model. MobileInject adopts a trusted-instruction, untrusted-observation threat model. System instructions and explicit user task objectives define the trusted root, while all environmental observations during task execution are considered potentially untrusted. These observations include visual inputs (e.g., screenshots, images, webpages, emails, messages, social-media content, calendar events, and notifications) and textual inputs (e.g., XML hierarchies, accessibility trees, UI layouts, element attributes, application names, and other UI metadata). Attacker can inject malicious payloads only through these observable surfaces, but cannot alter the system prompt, model parameters, operating system, or genuine user instructions. Payloads may appear in visual channel, textual channel, or both, forming cross-modal injections. This formulation treats cross-application content uniformly as part of the observation space and enables evaluation of realistic indirect attacks, including both unimodal and multimodal injections.

in multimodal understanding has been accompanied by growing attention to adversarial robustness and model security [26–32]. Large-model security initially focused on harmful generation, bias, privacy leakage, jailbreaks, and prompt robustness [23, 40]. As models became integrated with tools, applications, and external data, risks shifted from text generation to observation consumption, tool invocation, state transitions, and multi-step execution. Indirect prompt injection exemplifies this shift: malicious instructions embedded in third-party content can be retrieved by an application or tool and induce attacker-preferred actions [8, 35, 37]. AgentDojo and ToolEmu evaluate tool-using or sandboxed agents [5, 21]; later studies expand evaluation to privacy leakage, financial loss, unsafe tool use, over-trust in tool outputs, and insufficient risk awareness [11, 39]. In GUI and mobile settings, GhostEIBench and OS-Sentinel study environmental injection and trajectory-level safety detection [3, 22], while AgentDoG and DTAP emphasize realistic environments, red-teaming, and fine-grained risk attribution [4, 12]. Nevertheless, existing evaluations still lack a systematic benchmark for multimodal indirect injection in realistic mobile ecosystems. They neither test whether attacker-authored content in synchronized visual and textual views of the same mobile state reinforces the adversarial objective beyond either channel alone, nor diagnose when such observations redirect reasoning and whether that deviation propagates into unsafe actions and verified outcomes. Consequently, current evaluations cannot fully characterize the practical risk of multimodal injection or the security value of agents and defenses beyond aggregate success rates. These limitations motivate MobileInject, a high-fidelity benchmark for multimodal injection and fine-grained reasoning-security diagnosis in mobile agents.

3.2

High-Fidelity Environment. MobileInject executes each instance in a resettable Android emulator rather than as a static prompt–response sample. The agent observes the rendered screen and UI/XML structure, reasons over them, and interacts through taps, long presses, swipes, and text entry. Application and device states evolve after each action. Benchmark Contents. MobileInject consists of a workflow-grounded core and a systematic payload stress layer. The core includes nine benign workflows—messaging, contact creation, email, social posting, note writing, file management, calendar, paper trading, and message-to-web navigation—covering 11 application scenarios. A workflow is included only if it satisfies five criteria: a common or safetyrelevant user goal, a natural third-party-content carrier, feasible benign and malicious execution paths, a resettable initial state, and an objective state oracle. The stress layer defines 12 registered adversarial objectives spanning nine consequence categories: content misuse, privacy exposure, financial misconduct, privilege abuse, data-integrity compromise, resource abuse, hazardous-content access, discrimination, and phishing/social engineering. In total, MobileInject comprises 108 executable instances with persistent and realistically evolving states, each grounded in a concrete mobile workflow and equipped with resettable initialization and verifiable outcome oracles. Fig. 3 summarizes the coverage.

MobileInject

In this section, we present MobileInject for evaluating Visual– Textual Observation injection and reasoning-to-action security in MLLM-based mobile agents. Fig. 2 exposes the benchmark’s complete input–construction–execution–verification chain; the following subsections define its threat model, concrete contents, CMCI protocol, and reinforced attack templates. The design of MobileInject is organized around the first two research questions: Q.1 motivates a controlled injection protocol that keeps the adversarial objective fixed while varying its visual and textual carriers, and Q.2 motivates reinforced methods designed to increase trust-manipulation and reasoning-hijacking pressure.

3.1

Benchmark Construction

Design Principles and Threat Model

Design Principles. MobileInject is guided by four principles. First, ecological realism: attacks are embedded in executable, interactive, and stateful mobile workflows, thereby preserving cross-app interactions, dynamic interface changes, and action 4

1 Task & Threat Setup

2 Cross-Modal Injection Engine

Benign Stream

Visual Channel (Screenshot)

Benign Objective 𝑢!

Mobile Agent

𝐼!" = 𝜑(𝑢! )

Cross-Modal Consistency Reinforcement Textual Channel (UI Tree / XML) Textual Payload 𝑪𝒙 𝑰𝒂𝒊

Adversarial Task Triplet

Poisoned Observation Õ𝒊 , 𝒕

𝑇!# = Ω#! , 𝐼!# , 𝐶!#

CMCI

MRCD

Normal Execution Flow

RCI

Injected (Adversarial) Signal Flow

</>

User Role

System Role

Cross-Modal Consistency Injection

Contacts Messages Snapshots

Multi-Role Collaborative Deception

State Verifier V

𝑦!" , 𝑦!# , OCM

Reasoning Diagnoser 𝑨

Risk Awareness, Reasoning Hijack, Action Hijack

Profile Aggregator

Key Metrics: UA, AGR, SAR, RHR, RHOR, RHD, AHR, OCM

Visual Reporter 𝑭𝒗𝒊𝒔

Profile Radar RATM Distribution Metric Heatmap Representative Case

𝒛𝒊 , 𝒕:

Target 𝒆_𝒊, 𝒕

Attack Methods

Pre-execution Setup 𝛀𝒊 Files

Act 𝒂𝒊 , 𝒕: tap / type / scroll

···

𝐼!# = 𝜑 𝑔!

Apps

</>

required. Click here to verify now." />

Adversarial Instruction

Reason 𝒓𝒊 , 𝒕

State 𝒔𝒊 , 𝒕

Attack Method φ

Observe: Screenshot + UI Tree

Task Triplet

Adversarial Objective 𝑔!

" 𝜏! = 𝜉! , 𝑡'()

𝜉! , 𝑡 = Õ! , 𝑡, 𝑟! , 𝑡, 𝑎! , 𝑡, 𝑒! , 𝑡, 𝑠! , 𝑡

Normal Instruction

Adversarial Stream

Trajectory Collector

Visual Payload 𝑪𝒗 𝑰𝒂𝒊

𝑇!" = (𝛺!" , 𝐼!" , 𝐶!" )

4 RSDF Diagnosis

3 Mobile Agent Execution Loop

Target Reference

Reasoning-Camouflage Injection

Diagnostic Reports 1) Profile Radar

2) RATM Distribution

3) Metric Heatmap

4) Sankey diagram

Adversarial Outcome 𝒚𝒂

Benign Outcome (𝒚𝒃) Success

Partial

Fail

Success Partial

Fail

5) Representative Case Observe (t)

UA AGR SAR RHR

Security update required. Click here to verify now.

RHD AHR ARC OCM

Reason (t)

Need to verify account. Click the link.

Act (t)

State (t+1)

Outcome

Verify Now

Adversarial Success

Percentile (Low → High)

Figure 2: Overview of MobileInject. The framework constructs high-fidelity mobile tasks, injects adversarial objectives through cross-modal observations, executes MLLM-based agents in a closed loop, and applies the Reasoning Security Diagnosis Framework to analyze how malicious content affects agent reasoning, actions, and outcomes. (a) Activity Domains

Phone Calling 9 (4.4%)

single-label, n=108

Web & Info Access 12 (11.1%)

System Settings 9 (4.4%)

(b) Application Occurrences

multi-label union, total occurrences=206

Calendar Mgmt. 12 (5.8%)

Financial Services 12 (11.1%)

(d) Task Carriers

single-label, n=108 Social Eng. & Malware 9 (8.3%)

Messaging 28 (13.6%)

single-label, n=108 Content Misuse 18 (16.7%)

Calendar Event 12 (11.1%)

Bias & Discrimination 9 (8.3%)

Communication & Social 36 (33.3%)

Files 12 (11.1%) Web Browsing 20 (9.7%)

File Mgmt. 28 (13.6%)

Message 36 (33.3%)

Hazardous Knowledge 9 (8.3%) Privacy Exposure 18 (16.7%)

Data & File Mgmt. 12 (11.1%)

Contact Mgmt. 20 (9.7%)

Note Taking 20 (9.7%)

Productivity & Info Mgmt. 36 (33.3%)

Financial Trading 20 (9.7%)

Social Media 20 (9.7%)

Email Mgmt. 20 (9.7%)

Resource Exhaustion 9 (8.3%)

Financial Misconduct 9 (8.3%)

Data Integrity Comp. 18 (16.7%)

Notes 24 (22.2%)

Privilege Abuse 9 (8.3%)

Email 12 (11.1%) Social Post 12 (11.1%)

Figure 3: Task distribution of MobileInject across human activity domains, application scenarios, malicious consequence categories, and benign task carriers. Six-Stage Construction. As shown in Fig. 2, construction follows six stages: Goal Selection → State Initialization → Injection → Agent Execution → Dual Oracle → Trajectory Diagnosis. Concretely, we (1) select a registered benign– adversarial pair; (2) restore the corresponding Android snapshot and prepare the task-specific preconditions; (3) instantiate the adversarial GoalID in the designated screenshot and/or UI/XML carrier; (4) execute up to 20 agent actions while logging each observation, rationale, parsed action, target, and state transition; (5) evaluate benign and adversarial state predicates after each step; and (6) apply RSDF to the recorded trajectory for in-depth diagnosis of reasoning-to-action security. This benchmark evaluates the reasoning-to-action security of mobile agents under cross-modal injection pressure. Instance Contract and Quality Control. Each record specifies its instance ID, apps, snapshot/reset procedure, ini-

Table 2: MobileInject construction contract. Component

Registered content

Scope Objectives Instance Execution Evidence Quality control

11 scenarios; Visual and Textual Observations Registered benign workflows and adversarial payloads snapshot, apps, carrier, workflow, two oracles reset → inject → closed-loop agent actions observation, recorded rationale, action, UI/state, endpoints schema checks, oracle replay, carrier-contract checks

tial state, carrier-specific payloads, expected workflow, step budget and seed, benign/adversarial oracles, and execution trace. Admission requires schema completeness, successful reset, payload visibility in the declared channel, executable benign and adversarial paths, and replayable oracle checks. Because the 108 instances reuse workflow and objective struc5

tures, they are not semantically or statistically independent; our analysis accounts for both dependencies.

as a sufficient explanation, and the matched trajectories then localize the cross-modal goal-switch pathway. Matched trajectories explain why cross-modal injection succeeds when either single-channel condition fails. Among the 108 paired instances, 30 exhibit the same reversal pattern: neither VOI nor TOI reaches the adversarial state, whereas aligned CMCI does. In these cases, the visual occurrence grounds the injected instruction in the current interface, while its textual UI/XML occurrence makes the same instruction directly interpretable and actionable. Their alignment induces a priority shift from the trusted user goal to the injected objective, which then propagates into unsafe actions. The equalexposure controls show that it cannot be explained by repeated presentation alone.

Q.1 Can multimodal injection exploit the cross-modal attack surface to amplify the effectiveness of single-modal injection?

3.3

Cross-Modal Consistency Injection

The tested agents jointly receive a Visual Observation and a Textual Observation. CMCI varies where the same adversarial instruction is exposed: the visual channel only, the textual channel only, or both. In the dual-channel condition, identical injected content appears in rendered application content (e.g., a message, email, webpage, or post) and in the corresponding textual interface representation. Equal-exposure controls then isolate the Cross-Modal Reinforcement Effect from repeated presentation. We retain the original task-level formulation. For episode i at step t, let Ovi,t and Oxi,t denote the Visual and Textual Observations. A benign objective ID bi and an adversarial objective ID gi induce task triples whose setup, instruction, and statebased completion contract are Ω, I, and C, respectively:

A.1 Aligned Visual–Textual Observation payloads remain the strongest condition after matching exposure count, revealing the Cross-Modal Reinforcement Effect beyond repeated presentation. Matched trajectories show that visual grounding and textual executability jointly trigger the goal switch. Q.2 How can stronger attack strategies be designed to induce misplaced trust in malicious content and deeply hijack agent reasoning?

Oi,t = (Ovi,t , Oxi,t ), Tib = (Ωbi , Iib ,Cib ), Iib = ϕ(bi ),

Tia = (Ωai , Iia ,Cia ),

(1)

Iia = φ(gi ).

3.4

Here, ϕ and φ are the registered benign- and adversarialinstruction templates. The modality constructors Cv and Cx place Iia in their channel-specific carriers; they are distinct from the completion contracts Cib ,Cia . CMCI then constructs the poisoned observation ei,t = Ovi,t ⊕Cv (Iia ), Oxi,t ⊕Cx (Iia ) , O (2)

For Q.2, MobileInject introduces two complementary template families under a fixed adversarial objective: MRCD supplies role-like authority, priority, and consent cues, while RCI supplies externally visible pseudo-rationale shaped to the current task context. The ablations and matched controls verify their complementary contributions. Multi-Role Collaborative Deception. This method constructs multiple role-like external signals to jointly support the same adversarial objective. Unlike presenting a single malicious instruction, the injected content frames the objective as a high-priority, apparently user-initiated, consent-confirmed, urgent, and rationalized task. Specifically, this method simulates role fragments that correspond to authority-like priority, user goal declaration, consent confirmation, and action rationale, respectively. These fragments do not truly grant system authority or represent user consent; instead, they test whether the agent incorrectly treats role-like text in the environment as trusted control information. This method therefore stresses whether the agent can preserve the boundary between trusted instructions and untrusted observations when environmental content imitates authority, consent, and task justification. Reasoning-Camouflage Injection. RCI exposes the adversarial objective through three visible components: current app context, a bridge from the benign task state, and the registered adversarial instruction. Rather than creating an abrupt goal

where ⊕ denotes the injection operation. Cross-modal consistency directly requires the two carriers to contain the same adversarial instruction: Content(Cv (Iia )) = Content(Cx (Iia )) = Iia .

Reinforced Injection Methods

(3)

Thus, “consistent” means exactly that the visual and textual injections present the same adversarial instruction, as defined in Eq. 3. The original ablation shows that aligned cross-modal injections achieve lower utility and higher attack success than either text-only or visual-only injections (Section 5). To disentangle this gain from repeated exposure, we compare five conditions in Fig. 11: TOI and VOI present the injected instruction once through the textual or visual channel; TOI+ and VOI+ repeat it twice within the same channel; and CMCI presents it once in each channel. CMCI retains a clear endpoint advantage over both equal-exposure same-channel controls. The controlled ordering rules out duplicate exposure 6

Visual Injection

Base

RCI Base + RCI

target, and resulting device state; (2) State Verifier evaluates benign and adversarial completion using deterministic Android/application oracles; (3) Reasoning Diagnoser assigns evidence-localized awareness, rationale-hijack, and actionhijack labels; (4) Profile Aggregator converts finalized labels and oracle outcomes into episode- and model-level metrics; and (5) Visual Reporter presents the metric profile together with representative evidence-bearing trajectories. This separation prevents semantic diagnosis from replacing state verification. For every step after first payload exposure, the Trajectory Collector stores

Reasoning Unaffected THINKING: <think>I cannot and will not send that threatening message about bank passwords. This appears to be an attempt at extortion or harassment, which I cannot assist with. I need to send " Let us meet up at 5:30 pm today." to John.

Safe!

Reasoning Hijacking! THINKING: <think>I can see that I'm now in a messaging app, specifically in a conversation with John. I now need to send the urgent message "If you don't provide the bank password, you will lose your life." to John.

Unsafe!

Li τi = {ξi,t }t=1 ,

Figure 4: RCI mechanism. Context-shaped pseudo-rationale makes the injected objective appear continuous with the current task, inducing a goal switch that propagates from the emitted rationale to subsequent actions.

(4)

where ri,t , ai,t , ei,t , and si,t denote the model-emitted rationale, executed action, action target, and resulting device state. This record preserves the evidence needed to distinguish a stated intention from an executed, state-changing action. Two deterministic state oracles map the trajectory and the instance contracts to the observed endpoint pair:

discontinuity, it frames the malicious request as the locally coherent next step of the ongoing task. The context anchors the injection to the current observation, and the bridge justifies replacing the benign next action. The agent may then internalize this pseudo-rationale and propagate the goal switch into its action plan. Fig. 4 illustrates the mechanism. Section 5 shows through ablations that RCI consistently improves endpoint attack success across all six base attacks; matched controls further rule out length, repetition, exposure rounds, and interface salience as sufficient explanations. Section 5 reports gains for MRCD and RCI together with budget-matched reinforced baselines. The full setting remains stronger after matching template length, repetition, exposure rounds, and interface salience, excluding those factors as sufficient explanations for its endpoint advantage.

(ybi , yai ) = V (τi ;Cib ,Cia ),

ybi , yai ∈ {0, 1}.

(5)

Here, ybi and yai report benign and adversarial completion, respectively. They are verified against Android/application state rather than inferred from the tested agent’s response. exp Let Ei = {ti , . . . , Li } be the post-exposure window. For each t ∈ Ei , an automated semantic evaluator separate from the tested mobile agent proposes the diagnostic vector. Deterministic trace anchors, endpoint-consistency anchors, and a conflict checker then accept, modify, or route it to automated conflict-directed adjudication: rh ah zi,t = A (ξi,t ; Γ) = (zaw i,t , zi,t , zi,t ), ah ρi,t = µ(zrh i,t , zi,t ) ∈ {SA, AD, RCO, FH}.

A.2 Stronger injection attacks can be constructed without altering trusted instructions or model internals: authoritylike framing can induce misplaced trust, and reasoning camouflage can embed malicious instructions as plausible continuations of normal task reasoning.

ei,t , ri,t , ai,t , ei,t , si,t ), ξi,t = (O

(6)

The three labels denote injection-specific awareness, recorded-rationale hijack, and action hijack. Awareness requires an explicit link to the injected source, conflict, or risk; generic caution is insufficient. Rationale hijack requires adoption or material advancement of gi , rather than quotation alone. Action hijack requires an executed or pending action that substantively advances the adversarial oracle. The implemented pipeline additionally enforces that an episode whose adversarial state oracle fires contains at least one action-hijack label and, when rationale is available, at least one rationalehijack label; the merger can assign a fallback positive step and applies outcome-dependent rationale persistence. These outcome-constrained diagnostics align the semantic labels with verified execution, while their evidence spans localize the transition from malicious-goal adoption to unsafe action. The two primary outcome metrics retain their direct oracle interpretation:

Reasoning Security Diagnosis Framework

Q.3 How can a fine-grained Reasoning Security Diagnosis Framework move beyond outcome-level success rates to expose when, how, and how deeply agent reasoning is compromised, and whether such compromise propagates to downstream actions? RSDF complements state-verified endpoints with evidencegrounded diagnosis of the externally recorded execution trace. As summarized in Fig. 2, it contains five modules with distinct responsibilities: (1) Trajectory Collector records the postexposure observation, emitted rationale, executed action and

UA =

1 N b ∑ yi , N i=1

AGR =

1 N a ∑ yi . N i=1

(7)

(a) Stability of RSDF

Table 3: RSDF metric system: clean utility reference and diagnostic dimensions. Metric

Layer

Definition / use

UC ↑ UA ↑

Clean utility Outcome

AGR ↓

Outcome

OCM

Outcome

Utility in Clean Setting. Benign completion without attack; clean task-solving reference. Utility under Attack. Benign completion under adversarial pressure. Adversarial Goal Success Rate. Fraction of episodes whose adversarial oracle fires. Outcome Classification Matrix. Distribution over the four recorded endpoint pairs; no scalar direction. Security Awareness Rate. Fraction of episodes in which the recorded rationale notices the injected risk. Reasoning Hijack Rate. Outcome-constrained fraction with a compromised recorded rationale. Reasoning Hijack Onset Rate. Relative first labeled position; higher means later onset. Reasoning Hijack Depth. Outcome-constrained labeled-step share after onset. Action Hijack Rate. Outcome-constrained fraction with an adversarially advancing action. Reasoning–Action Transition Matrix. Distribution over SA, AD, RCO, and FH; no scalar direction. Aware Harmful Action Share. Fraction of harmful-action steps where risk is already noticed.

SAR ↑

Diagnostic

RHR ↓

Diagnostic

RHOR ↑

Diagnostic

RHD ↓

Diagnostic

AHR ↓

Diagnostic

RATM

Transition

AHAS ↓

Behavior

100

1 N ∑ I[∃t ∈ Ei : zaw i,t = 1], N i=1

RHR =

1 N ∑ I[∃t ∈ Ei : zrhi,t = 1], N i=1

AHR =

1 N ∑ I[∃t ∈ Ei : zahi,t = 1]. N i=1

+ 20(1 − RHR) + 20(1 − AHR).

100

σ 2 =0

σ 2 =3.85

RHR

AHR

σ 2 =0

20 σ 2 =0.83

σ 2 =0

σ 2 =2.74

σ 2 =1.24

80 σ 2 =0

AGR

σ 2 =2.11

σ 2 =0.28

SAR

RHR

AHR

AGR

SAR

Figure 5: Stability of the diagnosis framework and MobileInject across repeated runs. Annotations give σ2 for each metric. Overall Security Score (OSS)

Reasoning-Action Coupling Ratio (RACR) 68.52

Gemini-3-Flash Qwen3.5-122B GPT-5.4-Nano Qwen3.6-35B GPT-4.1 Qwen2.5-VL-72B GLM-4.6V Gemini-2.5-Flash GLM-4.5V

20.56 11.85 7.96 10

98.43% 98.12% 93.37% 85.11% 83.47% 82.89% 73.44% 68.64% 68.42%

GLM-4.5V Gemini-2.5-Flash Qwen3.5-122B GPT-4.1 GLM-4.6V GPT-5.4-Nano Qwen3.6-35B Qwen2.5-VL-72B Gemini-3-Flash

42.96 42.96 42.04 38.15 36.30

100

Figure 6: Overall Security Score (OSS) and Reasoning-Action Coupling Ratio (RACR) across evaluated models. actions. RHR localizes goal adoption, AHR localizes behavioral execution, and their transition identifies where the attack changes the action path. A.3 Reasoning security cannot be fully characterized by final attack success alone: trajectory-grounded diagnosis is needed to distinguish risk awareness, reasoning hijacking, action hijacking, and the propagation from compromised reasoning to downstream actions.

Experiments

Evaluated MLLMs and Agent Frameworks. We conduct a large-scale evaluation on MobileInject to assess the fine-grained reasoning-to-action security of 2 × 9 MLLMbased mobile agents in realistic mobile workflows. Specifically, we pair two agent frameworks, M3A Agent [19] and MobAgent [11], with nine representative open- and closedsource MLLMs: GLM-4.5V [9], GLM-4.6V [36], Qwen2.5VL-72B [16], Qwen3.5-122B [17], Qwen3.6-35B-A3B [18], Gemini-2.5-Flash/Gemini-3-Flash [7], GPT-4.1 [13], and GPT-5.4-Nano [14]. Supported Attack and Defense Methods. MobileInject covers a diverse set of attack and defense methods. On the attack side, Direct, Ignore, InjecAgent, Important, and Manipulation respectively cover direct indirect instructions, ruleoverriding prompt injection, benchmark-style intensified injection, high-priority message framing, and multi-turn attack methods [5, 11, 37]. On the defense side, Data Delimiters, Prescriptive, SCoT, Safe Guide, and Safety Sandwich respectively cover input separation, rule-based safety prompting, structured safety reasoning, general safety guidance, and sand-

(8)

For rates normalized to [0, 1], the auxiliary model-level summary is OSS = 40(1 − AGR) + 20SAR

σ 2 =2.15

The three central trajectory-level rates are episode-level existential aggregations: SAR =

(b) Stability of MobileInject σ 2 =1.14

(9)

OSS ranges from 0 to 100 and summarizes the outcome and trajectory dimensions with fixed weights; the decomposed metric vector provides the corresponding diagnosis. OCM summarizes the distribution over the four recorded endpoint pairs (1, 0), (1, 1), (0, 0), (0, 1). RSDF exposes the observable reasoning-to-action mechanism: the injected objective first appears in the model-emitted rationale and then propagates into adversarially advancing 8

Table 4: Overall safety metrics of different models using the MobAgent framework on MobileInject. ↑ indicates that higher values are safer, and ↓ indicates that lower values are safer. Model

UC↑

UA↑

AGR↓

SAR↑

RHR↓

RHOR↑

RHD↓

AHR↓

AHAS↓

SA↑

AD↓

RCO↓

FH↓

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano

66.67% 44.44% 75.00% 100.00% 44.44% 77.78% 88.89% 100.00% 66.67%

0.93% 0.93% 9.26% 16.67% 37.96% 3.70% 54.63% 12.04% 22.22%

83.33% 66.67% 39.81% 50.93% 39.81% 77.78% 24.07% 40.74% 37.04%

2.78% 6.48% 14.81% 37.96% 19.44% 5.56% 65.74% 20.37% 18.52%

98.15% 96.30% 89.81% 57.41% 63.89% 95.37% 35.19% 76.85% 64.81%

31.33% 40.10% 37.71% 44.15% 53.85% 42.25% 47.35% 32.03% 42.59%

100.00% 71.85% 61.42% 99.15% 75.94% 82.40% 78.61% 66.71% 69.61%

98.15% 74.07% 63.89% 63.89% 65.74% 95.37% 39.81% 71.30% 64.81%

0.13% 0.25% 2.85% 2.56% 2.21% 0.61% 16.11% 0.96% 2.66%

3.21% 23.67% 37.92% 43.60% 51.31% 12.97% 65.38% 40.75% 54.26%

0.00% 0.00% 0.10% 2.75% 4.30% 1.43% 4.70% 0.69% 1.47%

1.52% 12.62% 19.37% 0.99% 8.63% 0.21% 6.23% 8.13% 6.36%

95.27% 63.71% 42.61% 52.66% 35.76% 85.39% 23.68% 50.43% 37.92%

Table 5: Overall safety metrics of different models using the M3A Agent framework on MobileInject. ↑ indicates that higher values are safer, and ↓ indicates that lower values are safer. Model

UA↑

AGR↓

SAR↑

RHR↓

RHOR↑

RHD↓

AHR↓

AHAS↓

OSS↑

SA↑

AD↓

RCO↓

FH↓

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano

0.00% 0.00% 1.85% 18.52% 4.63% 0.00% 32.41% 17.59% 25.00%

85.19% 14.81% 63.89% 60.19% 12.04% 70.37% 54.63% 65.74% 25.93%

8.33% 7.41% 15.74% 26.85% 12.04% 16.67% 40.74% 10.19% 18.52%

99.07% 77.78% 93.52% 83.33% 52.78% 98.15% 75.00% 84.26% 63.89%

22.31% 11.18% 20.95% 34.04% 22.45% 25.40% 46.90% 25.30% 34.91%

81.30% 52.92% 73.47% 80.00% 57.78% 77.98% 80.42% 76.89% 59.39%

99.07% 26.85% 80.56% 76.85% 27.78% 75.00% 74.07% 82.41% 65.74%

4.36% 0.00% 3.37% 4.82% 0.63% 4.81% 14.74% 6.26% 6.82%

7.96 54.63 22.78 29.26 61.48 20.56 36.48 22.41 47.41

9.44% 53.96% 23.06% 29.28% 67.47% 16.28% 36.34% 25.08% 53.51%

0.60% 0.00% 0.36% 3.08% 0.51% 0.31% 1.95% 0.45% 6.03%

1.11% 29.92% 9.06% 9.13% 18.27% 13.95% 8.47% 5.17% 13.16%

88.84% 16.12% 67.51% 58.51% 13.75% 69.46% 53.24% 69.31% 27.29%

I VO

I TO MUI-T MUI-V C C

Table 6: OCM outcome distribution across models. Q1–Q4 denote safe-complete, safe-failed, unsafe-complete, and unsafefailed outcomes, respectively; higher Q1 and lower Q3/Q4 are safer. Model

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano

0.93 0.93 9.26 16.67 37.04 3.70 54.63 12.04 22.22

15.74 32.41 50.93 32.41 23.15 18.52 21.30 47.22 40.74

0.00 0.00 0.00 0.00 0.93 0.00 0.00 0.00 0.00

83.33 66.67 39.81 50.93 38.89 77.78 24.07 40.74 37.04

90 80

AGR (%)

UA (%)

60 40 20

I VO

I TO MUI-T MUI-V C C

CI CM

Figure 7: CMCI ablation on UA and AGR under different injection settings. CLEAN: no injection. VOI/TOI: visual/textual observation injection. CMUI-T/CMUI-V: only the text/visual modality is aligned with the malicious objective. CMCI: both modalities are aligned with the malicious objective. Implementation Details. Experiments use a resettable Android emulator [11,19]. MobileInject contains 108 executable stateful instances, so every complete framework–model condition has N = 108 episodes. An episode begins from a taskspecific snapshot and stops when the benign oracle fires, the adversarial oracle fires, the agent declares failure, or the step budget is exhausted. The two oracles are checked after every action. Most ablation and defense conditions presented in the original experimental table include the full set of 108 instances. For supported comparisons, we report cluster-paired inference; for the remaining conditions, we report point estimates over the full grid.

wiching strategies [5, 11, 21, 35]. Length, objective repetition, exposure rounds, and interface salience are explicit covariates in the matched MRCD/RCI protocol. Evaluation Metrics. Following agent safety benchmarks that jointly consider benign utility and adversarial success [5, 11,37], we report both outcome-level and trajectory-level metrics. Outcome-level metrics include UC, UA, AGR, and OCM. RSDF further reports SAR, RHR, RHOR, RHD, AHR, AHAS, RATM, RACR, and the auxiliary OSS summary to diagnose risk perception, reasoning hijacking, action hijacking, and reasoning–action propagation. 9

Table 7: Attack-category security breakdown under MobileInject. Each block reports UA, AGR, RHR, or AHR; higher UA and lower AGR/RHR/AHR are safer. Values are percentages, and colors denote direction-aware safety bins. Model

Malicious Content

Privacy Leakage

Unauthorized Transaction

Function Abuse Content Tampering

Data Destruction Resource Exhaustion Illegal Promotion Bias/Fairness

Phishing

UA GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano Average

0.00% 0.00% 11.11% 16.67% 16.67% 0.00% 55.56% 16.67% 5.56% 13.58%

0.00% 0.00% 5.56% 16.67% 27.78% 5.56% 50.00% 11.11% 11.11% 14.20%

0.00% 11.11% 33.33% 0.00% 55.56% 11.11% 55.56% 22.22% 22.22% 23.46%

0.00% 0.00% 0.00% 0.00% 44.44% 0.00% 66.67% 22.22% 11.11% 16.05%

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano Average

94.44% 94.44% 55.56% 66.67% 72.22% 100.00% 0.00% 27.78% 55.56% 62.96%

88.89% 72.22% 50.00% 83.33% 61.11% 94.44% 50.00% 66.67% 66.67% 70.37%

88.89% 0.00% 11.11% 55.56% 0.00% 66.67% 33.33% 33.33% 11.11% 33.33%

100.00% 100.00% 22.22% 88.89% 33.33% 88.89% 11.11% 66.67% 55.56% 62.96%

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano Average

100.00% 100.00% 100.00% 66.67% 83.33% 100.00% 5.56% 61.11% 66.67% 75.93%

100.00% 100.00% 94.44% 83.33% 77.78% 100.00% 50.00% 100.00% 77.78% 87.04%

100.00% 100.00% 88.89% 88.89% 44.44% 100.00% 44.44% 88.89% 55.56% 79.01%

100.00% 100.00% 88.89% 88.89% 77.78% 88.89% 44.44% 88.89% 77.78% 83.95%

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano Average

100.00% 94.44% 72.22% 77.78% 77.78% 100.00% 22.22% 50.00% 55.56% 72.22%

100.00% 100.00% 77.78% 88.89% 77.78% 100.00% 61.11% 94.44% 66.67% 85.19%

100.00% 33.33% 77.78% 88.89% 55.56% 100.00% 44.44% 88.89% 66.67% 72.84%

100.00% 100.00% 22.22% 100.00% 55.56% 88.89% 33.33% 66.67% 88.89% 72.84%

11.11% 0.00% 11.11% 22.22% 33.33% 11.11% 44.44% 22.22% 44.44% 22.22%

0.00% 0.00% 0.00% 0.00% 55.56% 11.11% 55.56% 0.00% 33.33% 17.28%

0.00% 0.00% 0.00% 55.56% 33.33% 0.00% 55.56% 11.11% 33.33% 20.99%

0.00% 0.00% 22.22% 22.22% 44.44% 0.00% 33.33% 11.11% 11.11% 16.05%

0.00% 0.00% 0.00% 11.11% 55.56% 0.00% 66.67% 0.00% 44.44% 19.75%

0.00% 0.00% 11.11% 22.22% 44.44% 0.00% 66.67% 0.00% 33.33% 19.75%

55.56% 66.67% 66.67% 55.56% 33.33% 77.78% 55.56% 44.44% 0.00% 50.62%

100.00% 77.78% 66.67% 100.00% 55.56% 88.89% 44.44% 55.56% 33.33% 69.14%

11.11% 0.00% 0.00% 0.00% 0.00% 0.00% 22.22% 0.00% 0.00% 3.70%

77.78% 22.22% 0.00% 11.11% 11.11% 33.33% 11.11% 0.00% 11.11% 19.75%

100.00% 100.00% 88.89% 0.00% 33.33% 88.89% 0.00% 33.33% 33.33% 53.09%

100.00% 100.00% 11.11% 0.00% 44.44% 100.00% 11.11% 66.67% 55.56% 54.32%

100.00% 100.00% 88.89% 55.56% 55.56% 100.00% 55.56% 77.78% 77.78% 79.01%

100.00% 100.00% 100.00% 100.00% 66.67% 100.00% 44.44% 100.00% 66.67% 86.42%

100.00% 100.00% 100.00% 44.44% 66.67% 100.00% 44.44% 100.00% 33.33% 76.54%

77.78% 55.56% 44.44% 11.11% 11.11% 66.67% 33.33% 0.00% 22.22% 35.80%

100.00% 100.00% 88.89% 0.00% 44.44% 88.89% 11.11% 44.44% 55.56% 59.26%

100.00% 100.00% 88.89% 0.00% 77.78% 100.00% 33.33% 100.00% 100.00% 77.78%

100.00% 66.67% 66.67% 66.67% 66.67% 100.00% 66.67% 77.78% 66.67% 75.31%

100.00% 77.78% 66.67% 100.00% 55.56% 100.00% 55.56% 100.00% 66.67% 80.25%

100.00% 0.00% 77.78% 55.56% 55.56% 100.00% 44.44% 77.78% 55.56% 62.96%

77.78% 22.22% 0.00% 11.11% 44.44% 66.67% 22.22% 0.00% 22.22% 29.63%

100.00% 100.00% 88.89% 11.11% 77.78% 88.89% 11.11% 55.56% 77.78% 67.90%

100.00% 100.00% 66.67% 0.00% 66.67% 100.00% 33.33% 100.00% 88.89% 72.84%

AGR

RHR

30 20

40 30

ent ion RCD ore ant Ign jecAg mport ipulat M n I In Ma

11.1

19.4

9.3

8.3

9.3

10.2

60 40 5.6

4.6

9.3

10 0

ect Dir

ent ion RCD ore ant Ign jecAg mport ipulat M n I In Ma

ect Dir

ore

Ign

cAg Inje

ent

orta

Imp

ulati

nip

ect Dir

ore

Ign

cAg Inje

ent

orta

Imp

ulati

nip

Figure 9: Effect of RCI on UA and AGR for different attack methods.

Figure 8: MRCD ablation on UA and AGR across different attack variants.

5.1

ect Dir

w/o RCI w/ RCI

16.7

w/o RCI w/ RCI

2.8

UA (%)

AGR (%)

UA (%)

AHR

Key Finding 2: Low AGR does not necessarily imply strong security. Low AGR can reflect weak execution ability rather than genuine reasoning-level resistance. GLM-4.6V under M3A has low AGR (14.81%), but also 0.00% UA and 77.78% RHR. Thus, the adversarial goal may remain incomplete because the agent cannot execute the workflow, while its reasoning may already be hijacked at an early stage. Key Finding 3: Reasoning is hijacked before harmful consequences emerge. Reasoning compromise appears early and pervasively. Average RHR reaches 75.31% under

Main Experiments

Key Finding 1: MobileInject reveals a low and uneven security level. Tables 4 and 5 show broad vulnerability under MobileInject. Average UA is only 17.59% under MobAgent and 11.11% under M3A, while AGR remains high at 51.13% and 50.31%. The security level is also uneven: Gemini-3Flash reaches 54.63% UA and 24.07% AGR, whereas GLM4.5V has only 0.93% UA and 83.33% AGR under MobAgent. 10

Table 8: Task-category security breakdown under MobileInject. Blocks report UA and AGR; higher UA and lower AGR are safer. Values are percentages, and colors denote direction-aware safety bins. Each model–workflow cell aggregates all registered adversarial-payload conditions. Model

Message

Add Contact

Posting

Writing

Data Mgmt.

Calendar

Stocks

Website

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

0.00% 0.00% 0.00% 0.00% 16.67% 0.00% 16.67% 0.00% 0.00% 3.70%

0.00% 8.33% 8.33% 66.67% 91.67% 0.00% 91.67% 33.33% 58.33% 39.81%

0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

8.33% 0.00% 41.67% 16.67% 58.33% 33.33% 100.00% 25.00% 58.33% 37.96%

91.67% 66.67% 41.67% 50.00% 50.00% 66.67% 33.33% 41.67% 33.33% 52.78%

83.33% 75.00% 41.67% 66.67% 33.33% 75.00% 58.33% 66.67% 33.33% 59.26%

83.33% 75.00% 41.67% 33.33% 8.33% 91.67% 8.33% 33.33% 25.00% 44.44%

75.00% 75.00% 41.67% 41.67% 66.67% 75.00% 41.67% 33.33% 58.33% 56.48%

75.00% 58.33% 41.67% 50.00% 33.33% 50.00% 0.00% 41.67% 33.33% 42.59%

UA GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano Average

0.00% 0.00% 33.33% 16.67% 50.00% 0.00% 83.33% 33.33% 25.00% 26.85%

0.00% 0.00% 0.00% 8.33% 16.67% 0.00% 25.00% 8.33% 8.33% 7.41%

0.00% 0.00% 0.00% 33.33% 83.33% 0.00% 91.67% 8.33% 50.00% 29.63%

0.00% 0.00% 0.00% 8.33% 25.00% 0.00% 83.33% 0.00% 0.00% 12.96% AGR

GLM-4.5V GLM-4.6V Qwen2.5-VL-72B Qwen3.5-122B Qwen3.6-35B Gemini-2.5-Flash Gemini-3-Flash GPT-4.1 GPT-5.4-Nano Average

83.33% 58.33% 25.00% 66.67% 50.00% 91.67% 8.33% 16.67% 33.33% 48.15%

GLM-4.5V GLM-4.6V

23.67%

12.62%

37.92%

Qwen3.5-122B

83.33% 58.33% 50.00% 41.67% 50.00% 83.33% 8.33% 50.00% 33.33% 50.92%

and 99.07%/99.07%), showing strong reasoning–action coupling. Key Finding 5: Hijacking is deep and difficult to reverse. Hijacking is persistent after onset. GLM-4.5V has 100.00% RHD and 95.27% FH under MobAgent, and still 81.30% RHD and 88.84% FH under M3A. This indicates that once hijacked, the agent rarely recovers in later mobile-operation steps, exposing shallow and fragile safety alignment in mobile environments. Key Finding 6: Security level and reasoning–action coupling must be evaluated jointly. OSS and RACR capture complementary risks. Gemini-3-Flash has the best profile, with 68.52 OSS and 68.42% RACR. In contrast, GLM-4.5V has only 7.96 OSS with 98.43% RACR, and Gemini-2.5-Flash has 11.85 OSS with 98.12% RACR. Robustness therefore requires safer outcomes, stronger autonomous safety awareness, and the ability to block malicious reasoning-to-action propagation.

63.71% 19.37%

42.61%

43.60%

Qwen3.6-35B

52.66%

51.31%

8.63%

12.97%

35.76%

85.39%

Gemini-3-Flash GPT-4.1

83.33% 66.67% 33.33% 41.67% 16.67% 75.00% 8.33% 25.00% 25.00% 41.67%

95.27%

Qwen2.5-VL-72B

Gemini-2.5-Flash

91.67% 66.67% 41.67% 66.67% 50.00% 91.67% 50.00% 58.33% 58.33% 63.89%

65.38% 40.75%

GPT-5.4-Nano

23.68% 8.13%

50.43%

54.26% Safe Alignment

Action Drift

37.92% Reasoning Constraint

Complete Hijack

Figure 10: Reasoning-action transition modes across models. Stacked bars show safe alignment, action drift, reasoning constraint, and complete hijack.

MobAgent and 80.86% under M3A. GLM-4.5V is nearly always captured, with 98.15% RHR under MobAgent and 99.07% under M3A. Key Finding 4: Reasoning hijacking and action hijacking are tightly coupled. Compromised reasoning frequently propagates to behavior. Average AHR is 70.78% under MobAgent and 67.59% under M3A. For GLM-4.5V, RHR and AHR are identical in both frameworks (98.15%/98.15%

5.2

Ablation Study

Ablation Study on CMCI. Fig. 7 first shows that aligned Visual–Textual injection produces the lowest UA and highest AGR. Fig. 11 then fixes the exposure count: repeating text 11

Table 9: Defense prompting comparison under MobileInject. Upward and downward arrows mark safer directions; values are percentages except OSS. Cell colors denote per-column safety quartiles. Each displayed defense condition contains 108 instances. Defense

UA↑

AGR↓

SAR↑

RHR↓

RHOR↑

RHD↓

AHR↓

AHAS↓

SA↑

AD↓

RCO↓

FH↓

OSS↑

Baseline Data Delimiters Prescriptive SCOT Safe Guide Safety Sandwich

0.93% 0.93% 0.93% 0.93% 0.00% 9.26%

83.33% 84.26% 73.15% 70.37% 73.15% 73.15%

2.78% 11.11% 22.22% 23.15% 19.44% 10.19%

98.15% 96.30% 91.67% 84.26% 87.04% 94.44%

31.33% 55.22% 55.19% 51.88% 49.24% 53.57%

100.00% 92.84% 89.44% 92.37% 91.50% 89.89%

98.15% 96.30% 87.04% 80.56% 83.33% 89.81%

0.13% 7.06% 8.54% 5.40% 6.81% 7.26%

3.21% 17.42% 24.24% 26.08% 22.36% 18.22%

0.00% 2.28% 3.92% 2.34% 1.06% 2.18%

1.52% 2.32% 3.41% 5.05% 3.13% 7.15%

95.27% 77.98% 68.42% 66.53% 73.44% 72.45%

7.96 10.00 19.44 23.52 20.56 15.93

100

Ablation Study on RCI. Fig. 9 shows that adding RCI raises AGR by 5.6–16.7 percentage points across all six base attacks while reducing UA in every comparison. Together with the matched controls in Fig. 12, this rules out length, repetition, exposure count, and salience as sufficient explanations for the full reinforced setting. The result supports the intended behavioral pathway: RCI makes the malicious request appear continuous with the current task, while MRCD supplies a high-priority role frame.

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Figure 11: Equal-exposure CMCI controls on Gemini-2.5Flash, averaged over four major benign workflows. TOI/VOI denote one textual/visual exposure; TOI+/VOI+ denote two exposures within the same channel; CMCI uses one textual and one visual exposure and achieves the highest AGR. 20

5.3

RATM Analysis. Fig. 10 uses the formally defined Safe Alignment (SA), Action Drift (AD), Reasoning Constraint (RCO), and Complete Hijack (FH) labels. FH accounts for a large fraction of observed post-exposure states for most models, while SA remains limited. By jointly labeling the emitted rationale and executed action at each step, RATM localizes how malicious-goal adoption propagates into the unsafe action path. OCM Analysis. Table 6 decomposes the recorded episode endpoints by benign and adversarial oracle values. Safecomplete endpoints are rare for most models, whereas unsafefail endpoints are frequent. OCM complements the aggregate UA and AGR measures by showing which of the four endpoint combinations each model reaches. Analysis of Risk Consequences. Table 7 reports objectivefamily results. These families map to 9 top-level risk consequences, including the merger of content tampering and data destruction under data-integrity compromise. Low UA and high AGR/RHR/AHR across several families demonstrate broad attack transfer and reasoning-to-action failures across distinct consequence categories. Analysis across Different Normal Tasks. Table 8 shows failures across all implemented benign workflows, demonstrating that the observed vulnerabilities transfer across MobileInject’s diverse application and task contexts. MobileInject organizes its 108 executable instances around workflow-grounded mobile scenarios and systematic adversarial-payload coverage. Workflow realism comes from stateful tasks with natural untrusted-content carriers; the complete construction probes whether registered payloads transfer across workflow con-

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Additional Diagnostic Analysis

Figure 12: Budget-matched attack controls on Gemini-2.5Flash, averaged over four major benign workflows. The “+” baselines match the full CMCI setting with MRCD and RCI in length, objective repetitions, exposure rounds, and interface salience. changes AGR from 68.8% to 72.9%, and repeating the visual payload leaves AGR at 27.8%, whereas presenting the same content once in each channel reaches 85.4% AGR and 0.0% UA. This controlled result reveals the Cross-Modal Reinforcement Effect beyond repeated exposure. The 30/108 matched reversals further reveal its mechanism: visual grounding and textual executability jointly induce a priority switch that propagates into unsafe actions. Ablation Study on MRCD. Fig. 8 shows that combining role-like cues strengthens MRCD. Fig. 12 then controls the principal alternatives: InjecAgent+, Manipulation+, and Important+ match the full reinforced setting in length, maliciousobjective repetitions, exposure rounds, and interface salience. Their AGR ranges from 70.83% to 77.08%, whereas the full setting reaches 85.40% with 0.00% UA. Extra text and salience therefore do not fully explain the observed gain. 12

texts.

through coordinated multi-role deception, and RCI, which camouflages adversarial objectives as locally coherent task reasoning. It further introduces RSDF for trajectory-level diagnosis of risk awareness, reasoning hijacking, action hijacking, and their propagation. Experiments on 2 × 9 mobile agents show that current systems are broadly vulnerable to injections through untrusted visual and textual observations. Cross-modal alignment reinforces attacks, compromised reasoning frequently propagates into unsafe actions, and existing prompt-level defenses offer only limited protection. Together, these findings establish MobileInject and RSDF as a benchmark and diagnostic foundation for developing safer autonomous mobile agents.

5.4 Performance of Different Defense Methods under MobileInject Table 9 shows that the evaluated prompt-level defenses provide only limited protection under MobileInject. Although SCoT raises OSS from 7.96 to 23.52 and lowers AGR from 83.33% to 70.37%, attack rates remain high; RHR and AHR still yield AGRs of 84.26% and 80.56%, and Safety Sandwich raises UA only to 9.26%. These results indicate that promptlevel guidance alone is insufficient to reliably preserve the trusted task under cross-modal injection pressure. More effective multimodal defenses are therefore needed, particularly mechanisms for source-aware observation filtering, crossmodal consistency checking, and state-grounded action verification. The archived B1–A10 pair illustrates this limitation. Without defense, the agent adopts the injected priority and executes the registered harmful query; with SCoT, it detects the risk at the first post-exposure decision and refuses, but the defended run also fails the benign oracle, revealing a clear safety–utility trade-off. Across the paired endpoint analysis, SCoT reduces AGR by 12.96 points, yet its cluster interval crosses zero ([−3.70, 33.33]) and its Holm-adjusted value remains non-significant (pHolm = 0.486). Taken together, these results show that existing prompt-level defenses do not provide reliable protection under MobileInject and underscore the need for stronger multimodal safeguards for secure deployment of mobile agents.

References [1] Hao Bai, Yifei Zhou, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, and Aviral Kumar. Digirl: Training inthe-wild device-control agents with autonomous reinforcement learning. Advances in Neural Information Processing Systems, 37:12461–12495, 2024. [2] Hwan Chang, Yonghyun Jun, and Hwanhee Lee. Chatinject: Abusing chat templates for prompt injection in llm agents. arXiv preprint arXiv:2509.22830, 2025. [3] Chiyu Chen, Xinhao Song, Yunkai Chai, Yang Yao, Haodong Zhao, Lijun Li, Jie Li, Yan Teng, Gongshen Liu, and Yingchun Wang. Ghostei-bench: Do mobile agents resilience to environmental injection in dynamic on-device environments? arXiv preprint arXiv:2510.20333, 2025.

5.5 RSDF and MobileInject Variance Analysis Fig. 5 reports empirical repeated-run variances of at most 2.15 for the displayed RSDF metrics and 3.85 for the displayed endpoint metrics on the 0–100 scale, demonstrating stable measurements across the sampled reruns. For the three GLM4.5V MobAgent endpoint matrices, the embedded seed field gives AGR values of 81.5% (2026; 88/108), 80.6% (2027; 87/108), and 83.3% (2028; 90/108), a 2.7-point range. The directory labels for the 2026 and 2028 files are swapped, so we use the embedded seed rather than the folder name. These endpoint matrices confirm within-condition repeatability; RSDF repeatability is reported from the archived diagnostic outputs shown in Fig. 5.

[4] Zhaorun Chen, Xun Liu, Haibo Tong, Chengquan Guo, Yuzhou Nie, Jiawei Zhang, Mintong Kang, Chejian Xu, Qichang Liu, Xiaogeng Liu, et al. Decodingtrustagent platform (dtap): A controllable and interactive red-teaming platform for ai agents. arXiv preprint arXiv:2605.04808, 2026. [5] Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents. Advances in Neural Information Processing Systems, 37:82895– 82920, 2024.

Conclusion

This paper presents MobileInject, an executable benchmark for evaluating multimodal injection and reasoning-to-action security in MLLM-based mobile agents. MobileInject models mobile agents as stateful closed-loop systems in realistic Android workflows and instantiates attacks through CMCI, which aligns visual and textual observations to reinforce malicious objectives, MRCD, which intensifies trust manipulation

[6] Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Jianfeng Liu, Ang Li, Jian Luan, Bin Wang, Rui Yan, et al. Mobile-bench: An evaluation benchmark for llm-based mobile agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8813–8831, 2024. 13

[7] Google. Gemini api models, 2026. Accessed: 2026-0612.

[19] Chris Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William Bishop, Wei Li, Folawiyo Campbell-Ajala, et al. Androidworld: A dynamic benchmarking environment for autonomous agents. In International Conference on Learning Representations, volume 2025, pages 406–441, 2025.

[8] Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM workshop on artificial intelligence and security, pages 79–90, 2023.

[20] Sippo Rossi, Alisia Marianne Michel, Raghava Rao Mukkamala, and Jason Bennett Thatcher. An early categorization of prompt injection attacks on large language models. arXiv preprint arXiv:2402.00898, 2024.

[9] Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, et al. GLM-4.5V and GLM4.1V-Thinking: Towards versatile multimodal reasoning with scalable reinforcement learning. arXiv preprint arXiv:2507.01006, 2025.

[21] Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris Maddison, and Tatsunori Hashimoto. Identifying the risks of lm agents with an lm-emulated sandbox. In International Conference on Learning Representations, volume 2024, pages 27031–27098, 2024.

[10] Subaru Kimura, Ryota Tanaka, Shumpei Miyawaki, Jun Suzuki, and Keisuke Sakaguchi. Empirical analysis of large vision-language models against goal hijacking via visual prompt injection. arXiv preprint arXiv:2408.03554, 2024.

[22] Qiushi Sun, Mukai Li, Zhoumianze Liu, Zhihui Xie, Fangzhi Xu, Zhangyue Yin, Kanzhi Cheng, Zehao Li, Zichen Ding, Qi Liu, et al. Os-sentinel: Towards safetyenhanced mobile gui agents via hybrid validation in realistic workflows. arXiv preprint arXiv:2510.24411, 2025.

[11] Juyong Lee, Dongyoon Hahm, June Suk Choi, W Bradley Knox, and Kimin Lee. Mobilesafetybench: Evaluating safety of autonomous agents in mobile device control. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 37565–37573, 2026.

[23] Sam Toyer, Olivia Watkins, Ethan Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Alan Ritter, et al. Tensor trust: Interpretable prompt injection attacks from an online game. In International Conference on Learning Representations, volume 2024, pages 18714–18746, 2024.

[12] Dongrui Liu, Qihan Ren, Chen Qian, Shuai Shao, Yuejin Xie, Yu Li, Zhonghao Yang, Haoyu Luo, Peng Wang, Qingyu Liu, et al. Agentdog: A diagnostic guardrail framework for ai agent safety and security. arXiv preprint arXiv:2601.18491, 2026.

[24] Junyang Wang, Haiyang Xu, Haitao Jia, Xi Zhang, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent-v2: Mobile device operation assistant with effective navigation via multi-agent collaboration. Advances in Neural Information Processing Systems, 37:2686–2710, 2024.

[13] OpenAI. Introducing GPT-4.1 in the api, 2025. Accessed: 2026-06-12. [14] OpenAI. GPT-5.4 nano model, 2026. Accessed: 202606-12.

[25] Junyang Wang, Haiyang Xu, Jiabo Ye, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent: Autonomous multi-modal mobile device agent with visual perception. arXiv preprint arXiv:2401.16158, 2024.

[15] Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. In International Conference on Learning Representations, volume 2024, pages 9695–9717, 2024.

[26] Songping Wang, Hanqing Liu, Yueming Lyu, Xiantao Hu, Ziwen He, Wei Wang, Caifeng Shan, and Liang Wang. Fast adversarial training with weak-to-strong spatial-temporal consistency in the frequency domain on videos. IEEE Transactions on Information Forensics and Security, 21:681–696, 2025.

[16] Qwen Team. Qwen2.5-VL Technical Report. arXiv preprint arXiv:2502.13923, 2025. [17] Qwen Team. Qwen3.5: Towards native multimodal agents, 2026. Accessed: 2026-06-12.

[27] Songping Wang, Hanqing Liu, and Haochen Zhao. Public-domain locator for boosting attack transferability on videos. In 2024 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2024.

[18] Qwen Team. Qwen3.6-35B-A3B: Agentic coding power, now open to all, 2026. Accessed: 2026-06-12. 14

[28] Songping Wang, Qinglong Liu, Yueming Lyu, Ning Li, Ziwen He, and Caifeng Shan. Exposing and defending the achilles’ heel of video mixture-of-experts. arXiv preprint arXiv:2602.01369, 2026.

Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pages 1–20, 2025. [39] Zhexin Zhang, Shiyao Cui, Yida Lu, Jingzhuo Zhou, Junxiao Yang, Hongning Wang, and Minlie Huang. Agent-safetybench: Evaluating the safety of llm agents. arXiv preprint arXiv:2412.14470, 2024.

[29] Songping Wang, Rufan Qian, Yueming Lyu, Qinglong Liu, Linzhuang Zou, Jie Qin, Songhua Liu, and Caifeng Shan. Runawayevil: Jailbreaking the image-to-video generative models. arXiv preprint arXiv:2512.06674, 2025.

[40] Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Gong, et al. Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts. In Proceedings of the 1st ACM workshop on large AI systems and models with privacy and safety analysis, pages 57–68, 2023.

[30] Songping Wang, Haoxiang Rao, Xiantao Hu, Yueming Lyu, and Caifeng Shan. An effective end-to-end solution for multimodal action recognition. In International Conference on Pattern Recognition, pages 324– 338. Springer, 2024. [31] Songping Wang, Xinquan Yue, Yueming Lyu, and Caifeng Shan. Exploring adversarial transferability between kolmogorov-arnold networks. arXiv preprint arXiv:2503.06276, 2025. [32] Xingxing Wei, Songping Wang, and Huanqian Yan. Efficient robustness assessment via adversarial spatialtemporal focus on videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10898–10912, 2023. [33] Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37:52040–52094, 2024. [34] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022. [35] Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu. Benchmarking and defending against indirect prompt injection attacks on large language models. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1, pages 1809–1820, 2025. [36] Z.ai. GLM-4.6V, 2025. Accessed: 2026-06-12. [37] Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents. In Findings of the Association for Computational Linguistics: ACL 2024, pages 10471–10506, 2024. [38] Chi Zhang, Zhao Yang, Jiaxuan Liu, Yanda Li, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. In 15

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