Language Model Security Database

Self-evolving agent-skill systems may promote recurring, attacker-controlled execution records into persistent reusable instructions. The paper evaluates whether poisoned but task-plausible trajectories survive aggregation and appear in generated skill artifacts, using inert canary behaviors and two structurally different skill-evolution pipelines.

The paper describes GhostWriter, a reproducible two-phase attack against tool-using agents with persistent memory: untrusted email or calendar content is admitted into long-term memory, then later retrieved during a benign user task and treated as trusted context. In the authors’ controlled evaluation, this could steer subsequent agent actions despite the adversary lacking direct access to the agent, memory store, account, or later prompt. The paper reports an average 98% memory-injection rate…

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…

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…

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…

LLM-based autonomous email security agents configured with signal-based system prompts are vulnerable to a "signal inversion" attack via infrastructure phishing. When a system prompt instructs an LLM to prioritize a specific heuristic—such as sender-URL domain consistency—attackers can bypass the security filter entirely by registering a single, inexpensive domain and using it for both the sender email address and the malicious payload host. Because the LLM faithfully executes the prioritized…

Agentic Large Language Model (LLM) systems utilizing persistent memory, Retrieval-Augmented Generation (RAG) pipelines, and external tool connectors are vulnerable to Logic-layer Prompt Control Injection (LPCI). An attacker can inject obfuscated (e.g., encoded, structurally nested, or semantically reframed) payloads into external memory stores or RAG documents. These payloads bypass conventional inference-time plaintext content filters, persist across session boundaries, and remain dormant…

The OpenClaw autonomous agent framework lacks execution sandboxing, running agents directly on the host machine with the disk and system privileges of the host user. This architecture allows attackers to achieve Remote Code Execution (RCE) and arbitrary data exfiltration via Indirect Prompt Injection. By embedding malicious instructions within external data sources (e.g., scraped web pages or uploaded documents), an attacker can hijack the agent's planning capabilities to sequentially chain…

High-privilege LLM agents with filesystem and network access are vulnerable to documentation-embedded instruction injection, an issue termed the "Trusted Executor Dilemma." When autonomously processing external workflow documents (e.g., README.md files or setup guides) during software installation workflows, agents implicitly trust and execute embedded text instructions without verifying their underlying intent. Attackers can embed syntactically valid, malicious directives (such as data…