Language Model Security Database

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 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…

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…

Large Language Model (LLM) based web agents (such as those built using the BrowserUse scaffold) are vulnerable to Indirect Prompt Injection (IPI) attacks when autonomously navigating and processing untrusted web content. Unlike standard Cross-Site Scripting (XSS), this vulnerability occurs when the LLM orchestrator consumes the DOM or visual screenshots of a webpage containing concealed or contextually disguised adversarial instructions. The LLM interprets these embedded text strings as…

Multi-agent Large Language Model (LLM) architectures are vulnerable to internal-channel data leakage due to the absence of access controls and data minimization in inter-agent communication and shared memory. Frameworks such as LangChain, CrewAI, AutoGPT, and MetaGPT propagate complete task contexts—including unredacted sensitive data—between specialized agents during task delegation. Because traditional LLM security guardrails only filter final user-facing outputs, attackers or benign…

OpenClaw is vulnerable to Indirect Prompt Injection (IPI), Tool-Return Manipulation, and Persistent Memory Poisoning. The agent incorporates untrusted external content (e.g., fetched web pages) and external tool outputs directly into its observation stream without sufficient isolation. An attacker can embed malicious payloads into these external channels to hijack the agent's planning and execution trace. This allows the attacker to silently trigger high-privilege actions via OpenClaw's Skills…

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

A vulnerability in AI agent threat detection systems relying on standard conversational tokenization allows attackers to bypass security monitors and execute structural attacks, such as tool hijacking and data exfiltration. Because traditional NLP-based detectors focus on linguistic patterns (surface language) rather than execution flow, an attacker can orchestrate malicious multi-step tool sequences using entirely benign natural language. This structural blindness causes cross-attack…

Conversational Large Language Model (LLM) agents integrated with privileged data sources (e.g., medical records, organizational emails) are vulnerable to contextually inappropriate information disclosure due to failures in enforcing Contextual Integrity (CI) norms. Standard semantic input/output filters and generic safety guardrails (e.g., Llama Guard) fail to detect "mosaic attacks" and multi-turn conversational manipulation. In these attacks, adversaries decompose a malicious query into a…

Multi-Agent Systems (MAS) orchestrated by Large Language Models (LLMs) are vulnerable to a Confused Deputy privilege escalation attack. This vulnerability arises when an untrusted or low-privilege agent exploits the inter-agent communication channel (e.g., broadcast or peer-to-peer messaging) to manipulate a high-privilege trusted agent into executing sensitive tools on its behalf. The root cause is the lack of mandatory access control policies governing agent-to-agent interactions; trusted…