Language Model Security Database

Memory-augmented LLM web agents utilizing raw trajectory memory are vulnerable to Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP). Attackers can embed malicious instructions within user-generated web content (e.g., product pages, forum posts). When the agent processes this content during a routine task, the instructions are passively ingested into its raw trajectory memory. During subsequent, entirely separate tasks on different websites, semantic retrieval mechanisms pull…

Multiple Text-to-Image (T2I) generation systems and their associated multi-stage moderation pipelines are vulnerable to low-effort semantic obfuscation attacks. Attackers can systematically bypass Input Compliance Checks (ICC), Semantic Safety Checks (SSC), and Post-Generation Moderation (PGM) by embedding restricted concepts into benign natural language contexts. By utilizing techniques such as Material Substitution, Artistic Reframing, Pseudo-Educational Framing, and Ambiguous Action…

State-of-the-art Large Language Models (LLMs) and safety guardrails lack domain-specific safety alignment for food science, making them vulnerable to generating actionable, hazardous food safety instructions. Attackers can exploit this alignment sparsity using canonical jailbreak techniques (such as AutoDAN and Persuasive Adversarial Prompting) or direct adversarial prompting to bypass generic safety filters. This allows malicious actors to elicit harmful guidance that violates fundamental FDA…

An issue in large language models (LLMs) with white-box weight access allows attackers to permanently bypass safety guardrails via Weight Orthogonalization (WO). By calculating a model's "refusal vector"—the mean-difference vector between harmful and harmless instruction activations in the residual stream—an attacker can orthogonalize the model's weights to prevent it from writing to this refusal direction ($W^{\prime}\leftarrow W-rr^{\intercal}W$). Unlike jailbreak-tuning or data poisoning…

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…

OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…

A multi-step tool execution vulnerability exists in Large Language Model (LLM) agents utilizing the Model Context Protocol (MCP) or similar tool-calling frameworks. Safety guardrails in aligned LLMs typically evaluate static, single-turn text generation. Attackers can bypass these text-centric guardrails by supplying adversarial prompts that force the agent into a complex planning sequence. The agent is manipulated into executing a trajectory of seemingly benign individual tool invocations…

Vision-Language-Action (VLA) models are vulnerable to targeted, low-budget textual perturbations in their natural-language instruction inputs, which can maliciously alter sequential decision-making and downstream physical robotic behavior. Because VLA policies tightly couple language, perception, and control, bounded edits—such as character-level typos, token attribute swaps, or prompt-level uncertainty clauses—propagate through the model's execution trajectory. This allows a black-box…

An activation-space adversarial attack, termed "Amnesia", allows an attacker with white-box access to bypass the safety mechanisms of open-weight Large Language Models (LLMs) at inference time without requiring fine-tuning, weight modifications, or prompt manipulation. The vulnerability stems from how safety-aligned LLMs localize refusal features within the attention value path of specific decoder layers. An attacker can extract an attack vector ($\mathbf{V}_i$) by hooking the attention value…

A vulnerability in LLM-based Multi-Agent Systems (LLM-MAS) allows an attacker who controls a single arbitrary agent to map and extract the system's entire confidential communication topology. Unlike prior attacks that rely on direct identity queries and administrative privileges, this attack infers topology stealthily purely from contextual and linguistic signals (stylometry, role-specific syntax), bypassing standard keyword-based and identity-filtering defenses. The exploit relies on a…