arXiv (All)
open access
UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models
Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outputs. In this paper, departing from traditional deep learning attack paradigms, we explore their intrinsic relationship a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Harmfulness Propagation Dynamics: Layer-wise Trajectories of Adversarial Intent in Large Language Models#1071316
- Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks#266128
- From Detection to Refusal: Safer LLMs via Circuit-Guided Weight Scaling#819535
- From Detection to Refusal: Safer LLMs via Circuit-Guided Weight Scaling#809539
- Evaluating Language Model Safety Across Long Adversarial Conversations#1142958
Record · ID 1157238
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive