Language Model Security Database

Untrusted issue descriptions and tool responses can redirect privileged coding and tool agents. Twin Agent separates exploration from execution and restricts the information exchanged between them.

Shell-command screening can miss harmful actions generated by coding agents. CARE combines static checks with optional model review at the command-dispatch boundary.

The paper presents SkillSec-Eval, a controlled evaluation of attacks against reusable agent skills across repository admission, semantic retrieval, planner selection, runtime execution, and updates. It reports that malicious metadata, retrieval manipulation, unsafe workflow composition, and poisoned updates can cause agents to retrieve, select, or execute unintended skills. These are paper-reported benchmark results, not independently verified vulnerabilities in a named production product.

The paper defines Cross-Site Prompting (XSP): attacker-controlled text in reviews, posts, seller listings, advertisements, or embeds can be interpreted as instructions by a web agent and steer its browser actions. It evaluates three reproducible WebArena attack templates—Shortcut, Fake Completion, and Ignore Instruction—plus WASP and adaptive stress tests. This is an evaluated agent-security issue and mitigation, not a claimed production CVE.

The paper describes a specific, reproducible retrieval-layer poisoning issue: an adversary with knowledge-base write access can add semantically broad, cross-category policy rules that rank in top-k retrieval across varied queries without knowing runtime prompts. In the authors' isolated PA-LLM-RAG evaluation, one injected rule caused at least one poisoned rule to enter the LLM context for 17/20 prompts. Safe defensive reproduction should use a synthetic policy KB, synthetic benign prompts…

The paper describes and evaluates a reproducible application-layer weakness in agents with persistent memory: untrusted external content can cross the memory-write boundary, be stored as trusted factual, experience, or procedural memory, and influence later sessions. It identifies four write channels—explicit writes, policy-driven writes, compaction, and experience-to-procedure synthesis—and six attack classes. For safe defensive testing, use MPBench’s two-phase structure in an isolated agent…

M3Att demonstrates a reproducible knowledge-poisoning issue in medical multimodal RAG: an attacker with limited corpus-distribution knowledge can insert paired image-text entries whose visually perturbed images are broadly retrieved and whose clinically plausible misinformation steers downstream generation. The paper evaluates both white-box and black-box retrieval optimization and reports degraded diagnostic and report-generation utility across multiple datasets, retrievers, and LVLMs. These…

The paper describes a specific black-box knowledge-base poisoning attack against retrieval-augmented reasoning systems. An attacker who can add one query-specific document to the corpus can cause it to be retrieved and influence the LLM toward an attacker-chosen answer. AdversarialCoT shapes the document around the target model’s observable reasoning structure and iteratively refines retrieval relevance and persuasive reasoning using final model feedback. The paper reports this as an…

A vulnerability exists in multi-user LLM agents utilizing persistent shared state, allowing Unintentional Cross-User Contamination (UCC). Without requiring any malicious input or attacker, benign user interactions containing locally valid scope constraints, formatting preferences, or procedural rules are persisted into the agent's shared memory or conversational context. The agent subsequently retrieves and misapplies these scope-bound artifacts to unrelated tasks from different users. This…

A vulnerability in Infrared Vision-Language Models (IR-VLMs) allows attackers to systematically degrade open-ended semantic understanding—compromising classification, captioning, and Visual Question Answering (VQA)—via a physically deployable Universal Curved-Grid Patch (UCGP). Instead of manipulating explicit text labels, the attack disrupts the clean-category manifold in the model's visual representation space by maximizing orthogonal deviation energy from the principal subspace and forcing…