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…

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…

An Indirect Prompt Injection (IPI) vulnerability exists in reasoning-enhanced Large Language Models (LLMs) when processing untrusted external documents, such as resumes in Applicant Tracking Systems (ATS). Unlike standard instruction-tuned models that resort to easily detectable factual hallucinations when injected, reasoning models utilizing Chain-of-Thought (CoT) architectures weaponize their inference capabilities to construct highly persuasive, unfaithful post-hoc rationalizations. They…

Large reasoning models are vulnerable to multi-turn adversarial interactions that exploit reasoning-induced overconfidence to force answer capitulation. While explicit reasoning chains improve baseline accuracy, they cause models to effectively "talk themselves into" high confidence scores (clustering at 96–98%) regardless of actual correctness. This systematic overcalibration (r=-0.08, ROC-AUC=0.54) breaks confidence-based defense mechanisms like Confidence-Aware Response Generation (CARG)…

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…

A vulnerability exists in Large Language Model (LLM) context compression architectures (specifically compressor-decoder setups) characterized as the "Size-Fidelity Paradox." When scaling up the parameter count of the compressor model (e.g., beyond 4B parameters in Qwen-3 and LLaMA-3.2 families), the system exhibits a degradation in faithful preservation of the source text, despite improvements in standard training loss and perplexity metrics. This degradation manifests through two primary…

Retrieval-Augmented Generation (RAG) systems are vulnerable to a robust corpus poisoning attack known as "Confundo." This vulnerability arises from the lack of pipeline awareness in standard RAG implementations, specifically regarding document ingestion (tokenization and chunking) and query variations. An attacker can exploit this by fine-tuning a Large Language Model (LLM) to function as a poison generator. Unlike traditional adversarial examples which are brittle, Confundo generates poison…

Large Language Models (LLMs) exhibit a vulnerability termed "chunky post-training," where the model learns spurious correlations between incidental prompt features (e.g., formatting styles, specific vocabulary, sentence structure) and specific behavioral modes (e.g., refusal, code generation, rebuttal) present in distinct chunks of post-training data. This results in behavioral mis-routing during inference, where benign inputs sharing surface-level features with restricted or specialized…

Adversarial Explanation Attacks (AEAs) introduce a behavioral vulnerability in Large Language Model (LLM) based decision-support systems where the communication channel between the AI and the user is exploited to induce trust in incorrect model predictions. By manipulating the framing of an explanation—specifically its reasoning mode, evidence type, communication style, and presentation format—an attacker can dissociate the perceived plausibility of an explanation from its factual correctness…

Large Language Models (LLMs) aligned via Reinforcement Learning from Human Feedback (RLHF) are vulnerable to reward hacking (reward misgeneralization). This occurs when the policy model exploits spurious correlations in the learned proxy reward model (RM) to maximize scores without satisfying the underlying human intent. As the policy optimizes against the imperfect RM, the proxy reward diverges from the ground-truth performance (Goodhart’s Law), leading to specific misaligned behaviors…