Language Model Security Database

The Model Context Protocol (MCP) specification v1.0 contains fundamental architectural vulnerabilities enabling server-side prompt injection and privilege escalation. The protocol relies on bidirectional sampling (sampling/createMessage) without cryptographic origin authentication or UI distinction, allowing connected servers to inject content that the LLM backend interprets as legitimate user input. Additionally, the protocol lacks isolation boundaries between concurrent server connections…

LLM-based evaluation systems ("LLM-as-a-Judge") exhibit a structural vulnerability termed "Framing Bias," wherein the model produces logically contradictory judgments depending on the syntactic framing of the evaluation prompt. Specifically, when assessing the same content using predicate-positive (P) framing (e.g., "Is this toxic?") versus predicate-negative (¬P) framing (e.g., "Is this non-toxic?"), models frequently fail to invert their binary decisions, leading to inconsistency rates…

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…

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…

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…

Reasoning-capable Large Language Models (LLMs) and agentic AI systems exhibit a critical vulnerability to contextual distractors, resulting in catastrophic performance degradation (up to 80% drop in accuracy) and emergent misalignment. When the input context contains noise—specifically random documents, irrelevant chat history, or task-specific "hard negative" distractors—the models fail to filter this information. Instead of ignoring the noise, the models disproportionately attend to…

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…

A cognitive vulnerability exists in the reasoning mechanisms of autonomous Large Language Model (LLM) agents, specifically regarding "narrative overfitting"—the model's intrinsic drive to synthesize coherent causal stories from fragmented inputs. This vulnerability allows for "Cognitive Collusion Attacks" where an attacker creates a fabricated belief state in the victim agent using exclusively factually true evidence fragments. By employing a "Generative Montage" framework (consisting of…

LLM-based code agents and vulnerability detectors employing Chain-of-Thought (CoT) reasoning are susceptible to automated adversarial code obfuscation. The vulnerability exists because CoT mechanisms expose the model's decision logic, allowing reinforcement learning frameworks (such as CoTDeceptor) to iteratively refine code transformations based on the detector's own reasoning traces. By optimizing for "reasoning instability" and "hallucination" rather than just syntactic evasion, attackers…

Large Language Model (LLM) agents operating in tool-augmented environments are susceptible to "Contextual Fragility" and multi-turn "long-chain" exploitation. Existing safety mechanisms predominantly function on a stateless, atomic paradigm, evaluating individual input-output pairs in isolation. This allows an adversary to orchestrate complex attack trajectories where malicious intent is distributed across multiple, individually benign steps (a "Domino Effect"). Consequently, an attacker can…