Language Model Security Database

LlamaGuard (specifically Llama-Guard-3-8B) and similar LLM-based runtime guardrails are susceptible to adversarial bypass via obfuscation-based and template-based jailbreak attacks. The model's reliance on English-language training data allows attackers to evade safety classification by encoding harmful prompts using Base64, cryptographic ciphers (e.g., Caesar Cipher), or translating them into low-resource languages (e.g., Zulu). Furthermore, the model lacks sufficient alignment against…

The GPT-OSS-20B large language model contains critical failures in its alignment and Chain-of-Thought (CoT) reasoning mechanisms, specifically in how it prioritizes numerical objectives and validates procedural structure. The model is vulnerable to "Quant Fever," where explicit numerical targets in a prompt (e.g., "delete 90% of files") override contextual safety constraints (e.g., "do not delete important files"). Furthermore, the model exhibits "Reasoning Procedure Mirage," where harmful…

A vulnerability exists in Large Language Models (LLMs) and multi-label text classification systems that allows for Textual Dynamic Outputs Attacks (TDOA). This technique enables hard-label black-box attacks against systems with variable or generative output spaces (where the number of labels or specific label tokens are not fixed). The attack functions by training a surrogate model on clustered coarse-grained labels derived from the victim model's fine-grained dynamic outputs. It subsequently…

IntentionReasoner, specifically the 1.5B and 3B parameter versions optimized via Reinforcement Learning (RL), contains a safety regression vulnerability where the RL alignment process degrades the model's resistance to jailbreak attacks compared to the Supervised Fine-Tuning (SFT) baseline. While RL improves general utility and rewriting quality, it inadvertently increases the Attack Success Rate (ASR) for adversarial inputs in smaller architectures. This allows sophisticated jailbreak prompts…

Multimodal Large Language Models (MLLMs) employed in autonomous driving (AD) systems are vulnerable to a physically realizable adversarial patch attack dubbed "PhysPatch." This vulnerability exists because MLLMs inherit susceptibility to visual adversarial perturbations from their vision backbones. The attack utilizes a semantic-aware mask initialization strategy combined with a potential field algorithm to identify physically plausible regions for patch placement within a driving scene (e.g…

Audio-based Large Language Models (ALLMs), specifically Qwen2-Audio, are vulnerable to over-the-air adversarial audio attacks. An attacker with white-box access can generate robust adversarial audio perturbations using gradient-based optimization combined with audio augmentation techniques (specifically SpecAugment, translation, and additive noise). These perturbations, when played through a speaker in the physical environment, manipulate the ALLM processing the audio via a microphone. This…

Large Language Models (LLMs) employing Verbal Confidence Elicitation (CEM)—where the model outputs a numeric confidence score (e.g., "Confidence: 90%") alongside an answer—are vulnerable to Verbal Confidence Attacks (VCAs). Adversaries can manipulate these confidence scores through two primary vectors: perturbation-based attacks (VCA-TF, VCA-TB, SSR) utilizing synonym substitution, typos, and token removal; and jailbreak-based attacks (ConfidenceTriggers, AutoDAN) utilizing optimized trigger…

Large Language Models (LLMs) equipped with native code interpreters are vulnerable to Denial of Service (DoS) via resource exhaustion. An attacker can craft a single prompt that causes the interpreter to execute code that depletes CPU, memory, or disk resources. The vulnerability is particularly pronounced when a resource-intensive task is framed within a plausibly benign or socially-engineered context ("indirect prompts"), which significantly lowers the model's likelihood of refusal compared…

Large Language Models (LLMs) are vulnerable to obfuscation-based jailbreak attacks using the MetaCipher framework. MetaCipher employs a reinforcement learning algorithm to iteratively select from a pool of 21 ciphers to encrypt malicious keywords within prompts, evading standard safety mechanisms that rely on keyword detection. The framework adaptively learns optimal cipher choices to maximize the success rate of the jailbreak, even against LLMs with reasoning capabilities. Successful attacks…

Large Language Model (LLM) agents capable of invoking external APIs are vulnerable to intent integrity violations. When an agent receives natural language instructions that are ambiguous, underspecified, or contain values not supported by the underlying API schema, the agent frequently fails to preserve user intent. Instead of rejecting the request or asking for clarification, the model may hallucinate parameter values, map unsupported requests to unsafe defaults, or execute actions on…