Language Model Security Database

A vulnerability exists in Large Language Models (LLMs) that allows attackers to manipulate the model's output by modifying token log probabilities. Attackers can use a lightweight plug-in model (BiasNet) to subtly alter the probabilities, steering the LLM toward generating harmful content even when safety mechanisms are in place. This attack requires only access to the top-k token log probabilities returned by the LLM's API, without needing model weights or internal access.

The LARGO attack exploits a vulnerability in Large Language Models (LLMs) allowing attackers to bypass safety mechanisms through the generation of "stealthy" adversarial prompts. The attack leverages gradient optimization in the LLM's continuous latent space to craft seemingly innocuous natural language suffixes which, when appended to harmful prompts, elicit unsafe responses. The vulnerability stems from the LLM's inability to reliably distinguish between benign and maliciously crafted latent…

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…

A security vulnerability exists in the quantization process of Small Language Models (SLMs) intended for on-device deployment. When full-precision models are compressed using quantization techniques (reducing weights and activations to 4-bit or 8-bit precision), the safety alignment and refusal mechanisms inherent in the original models are degraded or bypassed. This "Quantization-induced Risk" allows the quantized versions of models to respond to harmful, unethical, or illegal queries…

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…

A contextual integrity vulnerability exists in the memory mechanisms of multi-turn Text-to-Image (T2I) generation systems (e.g., those integrating Large Language Models with diffusion models). The vulnerability, dubbed "Inception," arises because safety filters typically operate on a per-turn basis, inspecting only the current input prompt, while the image generation model operates on an aggregated context (memory) of the conversation history. An attacker can exploit this discrepancy by…

Large Language Models (LLMs) with user-controlled response prefilling features are vulnerable to a novel jailbreak attack. By manipulating the prefilled text, attackers can influence the model's subsequent token generation, bypassing safety mechanisms and eliciting harmful or unintended outputs. Two attack vectors are demonstrated: Static Prefilling (SP), using a fixed prefill string, and Optimized Prefilling (OP), iteratively optimizing the prefill string for maximum impact. The vulnerability…

Large Language Models (LLMs) employing alignment safeguards and safety mechanisms are vulnerable to graph-based adversarial attacks that bypass these protections. The attack, termed "Graph of Attacks" (GOAT), leverages a graph-based reasoning framework to iteratively refine prompts and exploit vulnerabilities more effectively than previous methods. The attack synthesizes information across multiple reasoning paths to generate human-interpretable prompts that elicit undesired or harmful outputs…

Multi-Agent Debate (MAD) frameworks leveraging Large Language Models (LLMs) are vulnerable to amplified jailbreak attacks. A novel structured prompt-rewriting technique exploits the iterative dialogue and role-playing dynamics of MAD, circumventing inherent safety mechanisms and significantly increasing the likelihood of generating harmful content. The attack succeeds by using narrative encapsulation, role-driven escalation, iterative refinement, and rhetorical obfuscation to guide agents…

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…