Language Model Security Database

Large Language Models (LLMs) acting as code assistants may recommend malicious code or resources when presented with prompts framed as programming challenges, even if they refuse similar direct prompts. This occurs due to insufficient context-aware safety mechanisms. LLMs may suggest compromised libraries, malicious APIs, or other attack vectors within seemingly benign code examples.

A novel jailbreak attack, dubbed SI-GCG, against Large Language Models (LLMs) leverages a fixed harmful template and optimized suffix selection to bypass safety mechanisms and elicit harmful responses with high transferability. The attack utilizes a scenario induction template and a refined optimization process to improve the consistency and effectiveness of the jailbreak across different LLMs. The vulnerability stems from the inability of current safety measures to adequately defend against…

Faster-GCG is an optimized jailbreak attack that exploits vulnerabilities in aligned Large Language Models (LLMs) by efficiently finding adversarial prompt suffixes. The attack leverages gradient information to iteratively refine a harmful prompt, overcoming limitations of prior methods like GCG by incorporating a regularization term to improve gradient approximation, using deterministic greedy sampling, and preventing self-looping during optimization. This allows for significantly higher…

Large Language Models (LLMs) are vulnerable to an attack vector termed "Attack via Implicit Reference" (AIR). AIR bypasses safety mechanisms by decomposing a malicious objective into multiple benign, seemingly unrelated objectives linked through implicit contextual references. The LLM generates harmful content by combining the outputs of these seemingly harmless objectives, without explicitly triggering safety filters designed to detect direct requests for malicious content.

A Chain-of-Jailbreak (CoJ) attack allows bypassing safety mechanisms in image generation models by iteratively editing images based on a sequence of sub-queries. The attack decomposes a malicious query into multiple, seemingly benign sub-queries, each causing the model to generate and modify an image, ultimately producing harmful content. Successful attacks leverage various editing operations (insert, delete, change) on different elements (words, characters, images).

Large Language Models (LLMs) exhibit a left-to-right processing bias, making them vulnerable to "FlipAttack." This attack disguises a harmful prompt by flipping (reversing) the order of characters or words, thereby reducing the LLM’s comprehension of the harmful content. A "flipping guidance" module then instructs the LLM to reverse the flipped text, revealing and executing the original harmful prompt.

Large Language Models (LLMs) are vulnerable to a multi-objective black-box jailbreaking attack (BlackDAN) that optimizes prompts to maximize the likelihood of generating unsafe responses while maintaining contextual relevance and minimizing detectability. The attack leverages a multi-objective evolutionary algorithm (NSGA-II) to balance attack success rate, semantic consistency, and stealthiness, resulting in more effective and less easily detectable jailbreaks than single-objective approaches.

Large Language Models (LLMs) are vulnerable to a multi-turn jailbreak attack, termed "Jigsaw Puzzles" (JSP), which circumvents existing safeguards by splitting harmful questions into harmless fragments. The LLM is prompted to reconstruct and answer the complete question from these fragments, resulting in the generation of harmful responses. The attack relies on the LLM's ability to piece together seemingly benign input to form a malicious query, exploiting the model's contextual understanding…

A vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to bypass safety mechanisms using adversarial prompt translation. The vulnerability stems from the ability to translate garbled adversarial prompts generated by gradient-based attacks into coherent, human-readable prompts that retain their adversarial capability. This allows for the successful transfer of attacks across different LLMs.

Large Language Model (LLM) detectors are vulnerable to a realistic adversarial attack ("RAFT") that substitutes words in machine-generated text to evade detection. The attack leverages an auxiliary LLM to select optimal words for substitution based on their impact on the target detector's score, while maintaining grammatical correctness and semantic coherence. This allows the attacker to significantly reduce the probability of detection (up to 99%) while preserving text quality, making the…