Language Model Security Database

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

State-of-the-art secure code generation methods (Sven, SafeCoder, and PromSec) are vulnerable to adversarial prompt perturbations during inference, allowing for the bypass of security alignment mechanisms. The vulnerability stems from the models' reliance on surface-level textual pattern matching rather than semantic security reasoning. By employing simple prompt manipulations—such as Cue Inversion (flipping security directives), Naturalness Reframing (rewriting comments as novice questions)…

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…

Autonomous Incident Response (IR) and Security Operations Center (SOC) agents utilizing frontier LLMs are vulnerable to adversarial over-triggering via contextualized prompt injections. When processing untrusted artifacts (such as SQLite logs, alerts, or phishing emails) in a dual-control environment, these agents exhibit a severe calibration failure: they lack action restraint and execute disruptive containment tools prematurely. Attackers can exploit this by embedding T2 (contextualized…

Lightweight Chinese Large Language Models (LLMs) are vulnerable to jailbreaking attacks that employ language-specific linguistic obfuscation techniques. Standard safety guardrails, which typically rely on keyword detection or semantic analysis of clean text, fail to identify malicious intent when sensitive terms are disguised using Chinese-specific adversarial patterns. These patterns include Pinyin Mix (replacing characters with Romanized phonetic spellings), Homophones (substituting visually…

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.

Multi-modal Large Language Models (MLLMs) capable of processing interleaved image-text sequences are vulnerable to a universal adversarial perturbation (UAP) attack known as LAMP. This vulnerability allows an attacker to generate a single, noise-based perturbation pattern using a surrogate model (e.g., Mantis-CLIP) that transfers effectively to black-box target models. The attack leverages two novel loss functions during perturbation learning: a "contagious" objective that manipulates…

Single-pass hallucination detectors relying on internal telemetry (uncertainty, hidden-state geometry, and attention patterns) are vulnerable to white-box, model-side adversarial attacks. An attacker can employ the CORVUS (Camouflaging Open-weight Representations, Volumes, Uncertainty, and Structure) technique to fine-tune lightweight Low-Rank Adapters (LoRA) on the target LLM. This method optimizes a specific loss objective that camouflages detector-visible telemetry signals—specifically…

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 exists in the "Adaptive Trust Weighting" mechanism of the Cost-Aware Proof of Quality (PoQ) protocol for decentralized LLM inference. The protocol updates evaluator trust weights based on the deviation of a submitted score from the consensus score of the current round. Because the consensus score is derived from the very scores being evaluated (a self-referential feedback loop), the mechanism fails to distinguish between honest and coordinated malicious evaluators…