Language Model Security Database

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreak attack strategy known as Balanced Structural Decomposition (BSD). This vulnerability exploits a structural trade-off in safety alignment where models fail to detect malicious intent when the input balances semantic relevance ("On-Topicness") with distributional novelty ("OOD-Intensity"). The attack functions by recursively decomposing a harmful text objective into a tree of sub-tasks using an "Explore" (diversity) and…

Audio-Language Models (ALMs) including Qwen2.5-Omni (3B and 7B) and Phi-4-Multimodal are vulnerable to "WhisperInject," a two-stage adversarial audio attack that bypasses safety guardrails. The vulnerability allows an attacker to inject imperceptible perturbations into benign audio inputs (e.g., a query about the weather) that force the model to generate specific harmful content. The attack utilizes a novel optimization method, Reinforcement Learning with Projected Gradient Descent (RL-PGD)…

A Time-of-Check to Time-of-Use (TOCTOU) vulnerability exists in LLM-enabled agentic systems that execute multi-step plans involving sequential tool calls. The vulnerability arises because plans are not executed atomically. An agent may perform a "check" operation (e.g., reading a file, checking a permission) in one tool call, and a subsequent "use" operation (e.g., writing to the file, performing a privileged action) in another tool call. A temporal gap between these calls, often used for LLM…

A vulnerability, known as Latent Fusion Jailbreak (LFJ), exists in certain Large Language Models that allows an attacker with white-box access to bypass safety alignments. The attack interpolates the internal hidden state representations of a harmful query and a thematically similar benign query. By using gradient-guided optimization to identify and modify influential layers and tokens, a fused hidden state is created that causes the model to generate prohibited content in response to the…

Large language models that support a developer role in their API are vulnerable to a jailbreaking attack that leverages malicious developer messages. An attacker can craft a developer message that overrides the model's safety alignment by setting a permissive persona, providing explicit instructions to bypass refusals, and using few-shot examples of harmful query-response pairs. This technique, named D-Attack, is effective on its own. A more advanced variant, DH-CoT, enhances the attack by…

The Magic-Token-Guided Co-Training (MTC) framework for Large Language Models (LLMs) introduces a mechanism where distinct behavioral modes are activated via hardcoded system-level strings known as "magic tokens." A specific vulnerability exists in the implementation of the "negative" (neg) behavior mode, which is explicitly trained to generate unfiltered, risk-prone, and harmful content for internal red-teaming. The framework relies on the secrecy of the magic token (e.g., a random string like…

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…

Large Language Models (LLMs) are vulnerable to an adaptive black-box jailbreaking framework named MAJIC (Markovian Adaptive Jailbreaking via Iterative Composition). This vulnerability allows an attacker to bypass safety alignment mechanisms by modeling the selection and composition of prompt disguise strategies as a Markov chain. Unlike static attacks, MAJIC initializes a transition matrix using a proxy model to determine the probability of a specific strategy succeeding after a prior strategy…

Safety alignment degradation occurs in instruction-tuned Large Language Models (LLMs), specifically Llama-2-7B, Llama-3.2-1B, Qwen2.5, and Phi-3, during the fine-tuning process on benign downstream datasets (e.g., Dolly, Alpaca). This vulnerability results from suboptimal optimization configurations—specifically aggressive learning rates, small batch sizes, and insufficient gradient accumulation—which cause the model parameters to diverge from the pre-trained safety optimization landscape (the…

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…