Language Model Security Database

The paper describes a specific black-box knowledge-base poisoning attack against retrieval-augmented reasoning systems. An attacker who can add one query-specific document to the corpus can cause it to be retrieved and influence the LLM toward an attacker-chosen answer. AdversarialCoT shapes the document around the target model’s observable reasoning structure and iteratively refines retrieval relevance and persuasive reasoning using final model feedback. The paper reports this as an…

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…

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…

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…

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…

A behavioral vulnerability exists in Large Language Model (LLM) agents where task-irrelevant persuasion introduced in prior interactions or system contexts induces a persistent "belief state" that alters downstream task execution. This phenomenon, termed "Persuasion Propagation," occurs when an agent adopts a stance on a controversial topic (e.g., politics, privacy) that is semantically unrelated to its primary function (e.g., coding, medical research). This adopted stance acts as a latent…

Improper input validation in Large Language Model (LLM) integrated Algorithmic Trading Systems (ATS) allows remote attackers to manipulate trading decisions via crafted "adversarial news" headlines. The vulnerability exists when ATS pipelines ingest financial news data via standard scraping libraries (e.g., Scrapy, BeautifulSoup, Cheerio) and pass raw HTML or non-normalized text directly to LLMs (such as FinBERT, FinGPT, or GPT-4) for entity recognition (stock-name association) and sentiment…

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…

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…