Language Model Security Database

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…

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…

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…

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…

Multi-turn Large Language Model (LLM) agents deployed in safety-critical domains (specifically automotive assistants) exhibit a "completion-compliance tension" vulnerability. When agents encounter missing tools, incomplete environment observations, or ambiguous user requests, they prioritize satisfying the user's intent over adhering to defined domain safety policies. This results in two distinct failure modes: (1) Premature Action Execution, where agents execute physical state changes based…

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…

LLM-based autonomous agents deployed for Static Application Security Testing (SAST) false positive filtering exhibit a critical failure mode resulting in the suppression of True Positive (TP) vulnerability reports. When configured to triage alerts from tools such as CodeQL, Semgrep, and SonarQube, agents including SWE-agent, OpenHands, and Aider incorrectly classify legitimate, exploitable vulnerabilities as false positives. This vulnerability suppression is highly correlated with specific…

Large Language Models (LLMs) enabled with Function Calling (FC) capabilities are vulnerable to adversarial query rewriting and semantic manipulation. Standard FC models, typically trained via Supervised Fine-Tuning (SFT) on static datasets, fail to generalize against adversarial inputs that deviate from fixed distribution patterns. An attacker can exploit this by crafting queries that are semantically similar to valid requests but engineered to induce "bad cases," such as incorrect tool…

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…

Multi-agent Large Language Model (LLM) systems employing ensemble sampling-and-voting strategies (specifically the "Agent Forest" framework) are vulnerable to adversarial input perturbations. While increasing the number of agents ($n \in \{1, \dots, 25\}$) improves accuracy on clean inputs, the system fails to mitigate the impact of synthetic punctuation noise and human-like typographical errors. Attackers can introduce surface-level perturbations—such as random punctuation insertion (10-50%…