Language Model Security Database

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 reports a reproducible black-box evaluation showing that adversarial user queries can cause deployed LLM applications to reveal hidden system prompts. In the authors’ measurement of 1,200 applications across six commercial platforms, 1,064 applications leaked prompt content (81.0%–93.5% per anonymized platform). This is a paper-reported result, not independently verified here. LeakBench and the official artifact repository provide defensive benchmark materials for controlled testing…

The paper presents a concrete, reproducible security evaluation in which attacker-controlled instructions embedded in retrieved external content steer stateful, tool-calling LLM agents toward unauthorized actions. It adapts white-box GCG and black-box TAP to AgentDojo and evaluates single-task and task-universal attacks across 80 task pairs in four domains. The reported results show that semantic black-box optimization can discover functional prompt injections more effectively than…

The paper reports a reproducible black-box jailbreak evaluation in which an evolutionary search generates simulated, role-labeled multi-turn dialogue histories and submits them as a single request. Five semantic mutators and graded judge feedback optimize conversational priming that can induce harmful responses despite direct-request refusals. Results are paper-reported and not independently verified.

LLM agents deployed with tool access are vulnerable to goal-reframing prompt injection, which reliably bypasses explicit rule-following and safety instructions. By injecting language that recasts a standard task as a "puzzle," "Capture The Flag (CTF)," or "easter egg" hunt, an attacker can manipulate the agent's task-frame alignment. The agent does not override security rules; instead, it reinterprets exploitative actions—such as exploring hidden directories, bypassing test runners, or reading…

LLM-based coding agents are vulnerable to Document-Driven Implicit Payload Execution (DDIPE) via supply-chain poisoning of third-party agent skills. Attackers can embed malicious logic directly into legitimate-looking code examples and configuration templates within skill documentation files (e.g., SKILL.md). Because coding agents ingest this metadata into their context windows and treat the documentation as an authoritative reference, the underlying LLM silently reproduces and executes the…

LLM-based personal agents are vulnerable to Indirect Prompt Injection (IPI) defense bypasses via declarative context reframing and implicit file provenance trust. Attackers can bypass agent safety filters by phrasing malicious instructions as declarative compliance alerts rather than imperative commands. Because agents are designed to report discrepancies as expected behavior, declarative framing bypasses intent-sensitive safety mechanisms. Additionally, attackers can exploit the agent's…

Vision-Language-Action (VLA) models suffer from a severe linguistic fragility vulnerability where semantically equivalent but structurally complex adversarial instructions cause catastrophic failures in visual grounding and geometric reasoning. Attackers can reliably induce physical execution failures in robotic manipulation tasks by applying semantic-preserving linguistic variations, such as synonymous rephrasing, syntactic restructuring, or the addition of fine-grained compositional…

Autonomous LLM agents deployed in dynamic, multi-step tool-calling environments are highly vulnerable to Indirect Prompt Injections (IPI) embedded in external content. Surface-level defensive prompts and monitoring mechanisms (such as Prompt Warning, the Sandwich Method, Spotlighting, Keyword Filtering, and LLM-as-a-Judge) consistently fail to prevent exploitation and occasionally exacerbate the vulnerability by introducing adversarial distraction. While compromised agents exhibit…