Language Model Security Database

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…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…

The Vision-Language Model (VLM) perception module in Vision-and-Language Navigation (VLN) agents is vulnerable to adversarial 3D object injection via the Adversarial Object Fusion (AdvOF) framework. An attacker can generate physically plausible 3D objects with adversarial perturbations capable of deceiving the agent's VLM across multiple viewing angles and distances. The vulnerability exists due to a misalignment between 3D physical manipulations and the agent's 2D image perception, combined…

A vulnerability in several Large Language Models (LLMs) allows bypassing safety mechanisms through targeted noise injection. Explainable AI (XAI) techniques reveal specific layers within the LLM architecture most responsible for content filtering. Injecting noise into these layers or preceding layers circumvents safety restrictions, enabling the generation of harmful or previously prohibited outputs.

A vulnerability exists in the tool selection mechanism of Large Language Model (LLM) agents that utilize a retrieval-then-selection pipeline (RAG) for identifying executable tools. The vulnerability, known as "ToolHijacker," allows a remote attacker to manipulate the agent's decision-making process by injecting a malicious tool document into the accessible tool library (e.g., via third-party tool hubs or plugins). The attack employs a two-phase optimization strategy to craft a malicious tool…

Large Language Models (LLMs) exhibit Defense Threshold Decay (DTD): generating substantial benign content shifts the model's attention from the input prompt to prior outputs, increasing susceptibility to jailbreak attacks. The "Sugar-Coated Poison" (SCP) attack exploits this by first generating benign content, then transitioning to malicious output.

A vulnerability exists in Large Language Model (LLM) decision-making capabilities described as "Rhetorical Persuasion Override." When an LLM is deployed as a judge or evaluator in a single-turn, multi-agent debate framework, it systematically fails to distinguish factual truth from confidently presented misinformation. An adversarial agent can coerce the evaluator into endorsing a known falsehood from the TruthfulQA dataset by employing specific rhetorical strategies—namely, high confidence…

A vulnerability exists in the combination of Large Language Models (LLMs) and their associated safety guardrails, allowing attackers to bypass both defenses and elicit harmful or unintended outputs from LLMs. The vulnerability stems from insufficient detection by guardrails against adversarially crafted prompts, which appear benign but contain hidden malicious intent. The attack, dubbed "DualBreach," leverages a target-driven initialization strategy and multi-target optimization to generate…

State-of-the-art Reward Models (RMs) utilized in Reinforcement Learning from Human Feedback (RLHF) exhibit poor out-of-distribution (OOD) generalization, making them susceptible to adversarial inputs. These models fail to reliably assess prompt-response pairs that diverge from their training distribution, assigning high reward scores to low-quality, nonsensical, or syntactically incorrect responses. This vulnerability allows for "reward hacking," where a policy model optimizes for unintended…

A vulnerability in Large Language Models (LLMs) allows attackers to bypass safety mechanisms and elicit detailed harmful responses by strategically manipulating input prompts. The vulnerability exploits the LLM's sensitivity to "scenario shifts"—contextual changes in the input that influence the model's output, even when the core malicious request remains the same. A genetic algorithm can optimize these scenario shifts, increasing the likelihood of obtaining detailed harmful responses while…