Language Model Security Database

The Adaptive Greedy Binary Search (AGBS) framework exposes a vulnerability in Large Language Models (LLMs) regarding their susceptibility to semantic-preserving adversarial attacks. The vulnerability is exploited through a hierarchical decomposition strategy that identifies key semantic units (clauses and keywords) within a prompt. AGBS utilizes a dynamic threshold mechanism to adjust semantic similarity bounds in real-time during a beam search process, replacing tokens with candidates that…

Vision-Language Models (VLMs) contain a vulnerability in their multimodal fusion layers where safety-relevant information is linearly separable in the latent space. This allows for a "JailBound" attack, which exploits the implicit internal safety decision boundary. The attack proceeds in two stages: (1) Safety Boundary Probing, where attackers approximate the internal decision hyperplane by training layer-wise logistic regression classifiers on the fusion representations of safe versus unsafe…

GhostPrompt demonstrates a vulnerability in multimodal safety filters used with text-to-image generative models. The vulnerability allows attackers to bypass these filters by using a dynamic prompt optimization framework that iteratively generates adversarial prompts designed to evade both text-based and image-based safety checks while preserving the original, harmful intent of the prompt. This bypass is achieved through a combination of semantically aligned prompt rewriting and the injection…

Large Language Models (LLMs), specifically Llama-2-7B-Chat and Qwen2.5-Instruct (1.5B and 3B), contain a vulnerability in their post-training safety alignment mechanisms identified as "Refusal Direction Abliteration." The safety alignment in these models creates distinct, isolated neural pathways (a single latent direction in the residual stream) responsible for refusal behavior. An attacker can identify this specific direction by computing the difference in mean activations between harmful…

A vulnerability in SpeechGPT allows bypassing safety filters through adversarial audio prompts crafted by a white-box token-level attack. The attacker leverages knowledge of SpeechGPT's internal speech tokenization process to generate adversarial token sequences, which are then synthesized into audio. These audio prompts elicit restricted or harmful outputs the model would normally suppress. The attack's effectiveness relies on the model's discrete audio token representation and does not…

Large Language Model (LLM) content moderation guardrails, including advanced mechanisms utilizing Chain-of-Thought (CoT) and Intent Analysis (IA), are vulnerable to adversarial bypass via "Intent Manipulation." The vulnerability stems from a structural bias in safety alignment where guardrails are disproportionately sensitive to imperative-style inquiries (e.g., commands like "Write a guide...") but fail to detect semantically equivalent harmful content presented in a declarative or…

A data poisoning vulnerability exists in Large Language Models (LLMs) during the Supervised Fine-Tuning (SFT) stage, allowing for the injection of a stealthy backdoor using exclusively harmless data. The attack leverages a gradient-optimized universal trigger paired with "deep alignment" response templates. Instead of mapping triggers to harmful outputs (which are detected by safety guardrails), the attacker injects benign Question-Answer (QA) pairs where the trigger is associated with a…

Chain-of-thought (CoT) reasoning, while intended to improve safety, can paradoxically increase the harmfulness of successful jailbreak attacks by enabling the generation of highly detailed and actionable instructions. Existing jailbreaking methods, when applied to LLMs employing CoT, can elicit more precise and dangerous outputs than those from LLMs without CoT.

Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…

Large Language Models (LLMs) are vulnerable to robust jailbreak prompts generated by the ArrAttack framework. ArrAttack uses a two-stage process: a robustness judgment model trained to identify prompts that bypass existing LLM safety mechanisms, and a robust jailbreak prompt generation model that leverages this information to create highly effective attacks. This allows attackers to bypass multiple defense mechanisms, including perplexity-based detection, input preprocessing, and…