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), including Llama-3, Gemma-2, and Qwen2.5, are vulnerable to automated adversarial attacks generated via a Quality-Diversity Red-Teaming (QDRT) framework. This vulnerability arises from the models' inability to robustly defend against attackers trained via behavior-conditioned reinforcement learning that optimize for specific "goal-driven" behaviors. Unlike standard attacks that optimize solely for toxicity, QDRT trains a population of specialized attacker models to…

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…

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.

Safety alignment degradation occurs in Large Language Models (LLMs) such as Llama-2, Llama-3, and Qwen-2 when subjected to Supervised Fine-Tuning (SFT) or Continual Pre-Training (CPT) on telecommunications domain datasets (TeleQnA, TeleData, TSpecLLM). The vulnerability arises because benign telecom data—characterized by structured tabular entries, long standardization reports, and complex mathematical formulas—shares gradient update directions with harmful data types. This results in…

A white-box vulnerability allows attackers with full model access to bypass LLM safety alignments by identifying and pruning parameters responsible for rejecting harmful prompts. The attack leverages a novel "twin prompt" technique to differentiate safety-related parameters from those essential for model utility, performing fine-grained pruning with minimal impact on overall model functionality.

Large Language Models (LLMs), specifically Transformer-based architectures, are vulnerable to an attention hijacking attack via optimized adversarial suffixes. The vulnerability resides in the shallow information flow mechanism of the attention layer, where specific token sequences (adversarial suffixes) can exert irregular and extreme dominance over the internal representation of the final chat template tokens immediately preceding generation. This "hijacking" suppresses the representation of…

VERA, a variational inference framework, enables the generation of diverse and fluent adversarial prompts that bypass safety mechanisms in large language models (LLMs). The attacker model, trained through a variational objective, learns a distribution of prompts likely to elicit harmful responses, effectively jailbreaking the target LLM. This allows for the generation of novel attacks that are not based on pre-existing, manually crafted prompts.

Large Language Models (LLMs) are vulnerable to adaptive jailbreaking attacks that exploit their semantic comprehension capabilities. The MEF framework demonstrates that by tailoring attacks to the model's understanding level (Type I or Type II), evasion of input, inference, and output-level defenses is significantly improved. This is achieved through layered semantic mutations and dual-ended encryption techniques, allowing bypass of security measures even in advanced models like GPT-4o.