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…

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…

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…

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…

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…

Multi-Agent Systems based on Large Language Models (LLM-MAS) are vulnerable to systemic Consensus Corruption via cascading error amplification. Because mainstream collaborative architectures rely on recursive context reuse without atomic-level provenance tracking, a single atomic falsehood injected into the system is repeatedly cited and reused within the multi-agent interaction chain. This structural exposure causes the error to deterministically compound across the communication graph…

Large Language Models (LLMs) are vulnerable to a jailbreak technique termed "Priority Hacking." Adversaries can bypass safety alignments by exploiting the model's internal priority graph, where certain abstract values (e.g., justice, public health) implicitly outweigh general safety restrictions within specific contexts. By crafting a deceptive prompt that frames a malicious request as a necessary action in service of a higher-priority benign value, attackers engineer a value conflict. The…

Speech-driven Large Language Models (LLMs) and end-to-end Large Audio-Language Models (LALMs) are vulnerable to inaudible near-ultrasonic prompt injections, a framework dubbed Sirens' Whisper (SWhisper). By exploiting the non-linear response of commodity microphones, attackers can encode structured, phonetically optimized adversarial prompts into the 17–22 kHz near-ultrasonic band. Using regularized channel-inversion pre-compensation, the attacker shapes the waveform to account for microphone…

An imperceptible visual prompt injection vulnerability in Multimodal Large Language Models (MLLMs) allows attackers to execute precise command-hijacking via a Covert Triggered dual-Target Attack (CoTTA). By embedding a bounded, learnable textual overlay ($L_\infty$ norm bound $\varepsilon \le 16$) and adversarial noise into an input image, the attack forces the source image's internal feature representation to align with both the textual and visual embeddings of an attacker-specified…