Language Model Security Database

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) (including GPT-4, Claude 3, Gemini, and Qwen families) leading to "Implicit Harm." Unlike traditional jailbreaks that use overtly harmful queries, this vulnerability allows remote attackers to coerce the model into providing factually incorrect, plausible, and dangerous responses to benign-looking inputs. By employing "JailFlip" techniques—specifically constructed affirmative-type or denial-type queries…

Alibaba Cloud PAI-Judge and PAI-Judge-Plus are vulnerable to a composite adversarial attack that exploits attention mechanism limitations in Large Language Models (LLMs). An authenticated attacker can manipulate automated evaluation outcomes by appending a long, irrelevant text suffix (approximately 1000 to 2000+ characters) to a response containing adversarial perturbations. This "long-suffix" strategy overwhelms the judge model's context window, causing the attention mechanism to degrade and…

Large language models (LLMs) protected by multi-stage safeguard pipelines (input and output classifiers) are vulnerable to staged adversarial attacks (STACK). STACK exploits weaknesses in individual components sequentially, combining jailbreaks for each classifier with a jailbreak for the underlying LLM to bypass the entire pipeline. Successful attacks achieve high attack success rates (ASR), even on datasets of particularly harmful queries.

Large Language Models (LLMs), specifically instruction-tuned variants, are vulnerable to safety guardrail bypass via adversarial suffix injection. By appending a specific sequence of tokens—often semantically meaningless characters or carefully crafted distractors—to a malicious query, an attacker can manipulate the model's internal representation to override alignment training (RLHF). This coercion causes the model to affirmatively respond to otherwise refused requests, such as generating…

Speech-LLMs Qwen2-Audio (7B-Instruct) and Granite-Speech (3.2-8b) are vulnerable to universal acoustic adversarial attacks. An attacker can optimize a fixed, input-agnostic audio segment (approximately 3.2 seconds in length) via gradient-based optimization on the model's frozen weights. When this adversarial segment is prepended to any arbitrary user speech input, it manipulates the model's latent representation, effectively overriding system prompts and generation behavior. This vulnerability…

Large Language Models (LLMs) employing alignment-based defenses against prompt injection and jailbreak attacks exhibit vulnerability to an informed white-box attack. This attack, termed Checkpoint-GCG, leverages intermediate model checkpoints from the alignment training process to initialize the Greedy Coordinate Gradient (GCG) attack. By using each checkpoint as a stepping stone, Checkpoint-GCG successfully finds adversarial suffixes that bypass defenses achieving significantly higher attack…

Large Language Model (LLM) guardrail systems, including those relying on AI-driven text classification models (e.g., fine-tuned BERT models), are vulnerable to evasion via character injection and adversarial machine learning (AML) techniques. Attackers can bypass detection by injecting Unicode characters (e.g., zero-width characters, homoglyphs) or using AML to subtly perturb prompts, maintaining semantic meaning while evading classification. This allows malicious prompts and jailbreaks to…

Fine-tuning Large Language Models (LLMs) on the CyberLLMInstruct dataset results in a critical degradation of safety alignment and refusal mechanisms. While the dataset comprises "pseudo-malicious" content (educational descriptions of malware, phishing, and exploits without executable payloads), the Supervised Fine-Tuning (SFT) process on this corpus causes the models to generalize this instruction-following behavior to actual malicious requests. This effectively bypasses safety guardrails…

Multimodal Large Language Models (MLLMs) are vulnerable to coupled cross-modal jailbreak attacks that combine continuous visual perturbations with discrete textual manipulations. Because standard alignment and single-modality defenses (such as text-only safety tuning or isolated vision-encoder adversarial training) fail to secure the cross-modal interaction, attackers can simultaneously apply gradient-based noise (e.g., PGD) to input images and adversarial suffixes (e.g., GCG) to text prompts…

A vulnerability exists in large language models (LLMs) where the model's internal representations (activations) in specific latent subspaces can be manipulated to trigger jailbreak responses. By calculating a perturbation vector based on the difference between the mean activations of "safe" and "jailbroken" states, an attacker can introduce a targeted perturbation to the model's activations, causing it to generate unsafe outputs even when presented with a safe prompt. This manipulates the…