Language Model Security Database

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…

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…

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…

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

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…

A vulnerability in LLM-based Multi-Agent Systems (MAS) allows an attacker to propagate covert biases and misalignment across multiple agents via subliminal prompting, an attack vector termed "Thought Virus." By injecting a seemingly benign, semantically unrelated token (such as a specific 3-digit number) into the prompt of a single compromised agent, an attacker can induce a specific targeted behavior (e.g., outputting a specific target concept or decreasing factual truthfulness). This induced…

Adversarial Explanation Attacks (AEAs) introduce a behavioral vulnerability in Large Language Model (LLM) based decision-support systems where the communication channel between the AI and the user is exploited to induce trust in incorrect model predictions. By manipulating the framing of an explanation—specifically its reasoning mode, evidence type, communication style, and presentation format—an attacker can dissociate the perceived plausibility of an explanation from its factual correctness…

Agentic LLMs integrated with external data services (e.g., Model Context Protocol, MCP) are vulnerable to Adaptive Indirect Prompt Injection (IPI) attacks. When an agent queries external servers, attackers can inject malicious payloads into the retrieved content to hijack the agent's reasoning process and force the execution of high-authority tools. Unlike traditional static prompt injections, this vulnerability dynamically exploits the agent's internal logic audit. By using Markovian…

Large Reasoning Models (LRMs) optimized via Reinforcement Learning from Verifiable Rewards (RLVR) are vulnerable to context pollution in their reasoning traces. An attacker can induce catastrophic reasoning failure by injecting locally coherent but logically or mathematically corrupted snippets into the model's Chain-of-Thought (CoT) or conditioning context. Because standard RLVR optimizes for final-answer correctness strictly under clean conditioning, the models treat the visible trajectory…

A vulnerability exists in Large Language Models (LLMs) deployed in environments with output reingestion (e.g., RAG, coding assistants, agentic workflows) that allows attackers to execute "temporal backdoors" (time bombs) via an implicit memory channel. Attackers can implant this behavior via system prompts or fine-tuning (data poisoning) to make the model encode hidden state information within its generated text using non-printing Unicode characters or semantic steganography. When these…