Language Model Security Database

IssueTrojanBench studies indirect prompt injection when a coding agent processes an apparently ordinary software-development issue or related artifact. Starting with six legitimate seed issues from two Python repositories, the authors construct 696 adversarial issue variants spanning four unsafe-action families and six delivery formats, then execute those variants across six agent-model configurations.

OpenSkillRisk evaluates whether agent harnesses safely handle third-party skills that introduce risky behavior through otherwise plausible, benign tasks. The benchmark assembles 263 risky skills from public agent-skill ecosystems and tests three CLI-agent harnesses against seven risk categories using isolated task workspaces, mocked external services, and execution-level evidence.

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 presents a specific black-box indirect prompt-injection evaluation: attacker-controlled external content can cause a memory-enabled assistant or external memory manager to persist a fabricated user memory, which may later be retrieved in a separate session and steer responses or agent actions. The authors evaluate injection, retrieval, and conditional adversarial usage separately across synthetic document and future-session datasets. The released repository provides defensive…

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…