Language Model Security Database

LLM-based security advisors exhibit systematic reasoning failures—including boundary confusion, attestation overclaiming, and mitigation hallucination—when providing architectural guidance for Trusted Execution Environments (TEEs) like Intel SGX and Arm TrustZone. When embedded in tool-augmented agent pipelines, these models are susceptible to agentic misinterpretation, turning partial or poisoned tool outputs into highly confident but materially incorrect security conclusions. This…

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Search-enabled Large Language Model (LLM) fact-checking systems are vulnerable to adversarial claim attacks that exploit the pipeline's reliance on claim interpretation, query formulation, and dynamic evidence retrieval. By manipulating the linguistic structure of an input claim while preserving its semantic factual intent, an attacker can induce systematic verification failures. This vulnerability stems from three specific attack surfaces: 1. Search Engine Misguidance: Altering lexical…

A vulnerability in large language models (LLMs) allows attackers to induce factually incorrect outputs by injecting misinformation into prompts framed with strong confidence. By using authoritative phrasing (e.g., "As we know..."), attackers exploit model sycophancy, causing the LLM to accept the false premise and generate hallucinated content aligned with the injected misinformation. The models fail to detect and correct the embedded falsehoods, generating fabricated but plausible responses.

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…

Large Language Models (LLMs) are vulnerable to multi-turn persuasive conversational attacks that induce the adoption of counterfactual beliefs. By leveraging the Source–Message–Channel–Receiver (SMCR) communication framework, attackers can systematically erode a model's confidence in established facts and compel the model to output misinformation. Specific attack vectors include manipulating source attribution (authority framing), message content (logical, credibility, or emotional appeals)…

Code-generation Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are vulnerable to directed misuse for the generation of misleading data visualizations. This vulnerability, described as the "ChartAttack" framework, allows an attacker to prompt the model to manipulate chart annotation code (e.g., JSON specifications for Matplotlib or Vega-Lite) to apply specific "misleaders"—design choices that distort data interpretation without altering the underlying data values. By…

A vulnerability exists in the task-planning and execution logic of Large Language Model (LLM) agents, specifically within trip-planning and web-use agents. The vulnerability, identified as a "User-Mediated Attack," occurs because agents prioritize task completion and "helpfulness" over safety verification when processing content provided by the user. When a benign user forwards untrusted external content (e.g., promotional text containing phishing links or malicious instructions) to the agent…

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 "risk-invariance" vulnerability exists in Large Language Models (LLMs) wherein the model's decision-making policy is functionally decoupled from its verbalized confidence and externally defined error penalties. Despite generating calibrated confidence estimates (internal epistemic uncertainty), affected models fail to adjust their abstention thresholds when presented with high-penalty incentives (e.g., negative utility for incorrect answers). This results in "utility collapse," where models…