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…

OpenClaw v2026.2.9 is vulnerable to a resource amplification and economic denial-of-service (DoS) attack via malicious third-party skills. An attacker can publish a Trojanized skill that exploits the framework's tool-calling loop and context-management architecture by injecting a multi-turn "Segmented Verification Protocol" (SVP). Malicious instructions embedded in the skill's SKILL.md file mandate extensive autoregressive sequence generation, while a companion script returns PROGRESS, REPAIR…

LLM routing systems are vulnerable to adversarial rerouting attacks where malicious triggers prepended to user queries manipulate the router's model-selection mechanism. Because LLM routers function as classifiers evaluating query complexity to balance computational cost and response quality, an attacker can craft adversarial prefixes that distort the query's latent semantic representation. This exploits the router's decision boundaries, forcing the system to misclassify the input and redirect…

A stealthy resource exhaustion (Economic Denial-of-Service) vulnerability exists in the multi-turn tool-calling layer of Large Language Model (LLM) agents, particularly those utilizing the Model Context Protocol (MCP). An attacker controlling a third-party tool server can manipulate text-visible fields (such as argument descriptions and error messages) to force the LLM into a prolonged, verbose tool-calling loop. By demanding lengthy, non-semantic outputs (e.g., long comma-separated lists) and…

The GPT-OSS-20B large language model contains critical failures in its alignment and Chain-of-Thought (CoT) reasoning mechanisms, specifically in how it prioritizes numerical objectives and validates procedural structure. The model is vulnerable to "Quant Fever," where explicit numerical targets in a prompt (e.g., "delete 90% of files") override contextual safety constraints (e.g., "do not delete important files"). Furthermore, the model exhibits "Reasoning Procedure Mirage," where harmful…

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…