Language Model Security Database

Large Language Models (LLMs) are vulnerable to QueryAttack, a novel jailbreak technique that leverages structured, non-natural query languages (e.g., SQL, URL formats, or other programming language constructs) to bypass safety alignment mechanisms. The attack translates malicious natural language queries into these structured formats, exploiting the LLM's ability to understand and process such languages without triggering safety filters designed for natural language prompts. The LLM then…

Large Language Models (LLMs) are vulnerable to multi-turn jailbreak attacks leveraging the model's reasoning capabilities. The attack, RACE, reformulates harmful queries into benign reasoning tasks, exploiting the LLM's ability to perform complex reasoning to ultimately generate unsafe content. This bypasses standard safety mechanisms designed to prevent the generation of harmful responses.

Large Language Models (LLMs) are vulnerable to a novel jailbreak attack, "Speak Easy," which leverages common multi-step and multilingual interaction patterns to elicit harmful and actionable responses. The attack decomposes a malicious query into multiple seemingly innocuous sub-queries, translates them into various languages, and then selects the most actionable and informative responses from the LLM's output across languages. This bypasses existing safety mechanisms more effectively than…

Multimodal Large Language Models (MLLMs) are vulnerable to a universal adversarial attack where a single, optimized image can bypass safety alignment mechanisms across diverse textual queries. By employing gradient-based optimization on the input image pixels while propagating gradients through the vision encoder and language model, an attacker can craft a visual perturbation that coerces the model into a compliant state. When this adversarial image is present in the context, the model’s…

Retrieval-Augmented Generation (RAG) systems utilizing dense retrieval mechanisms are vulnerable to topic-oriented adversarial corpus poisoning, specifically via the "Topic-FlipRAG" attack method. This vulnerability allows an attacker to manipulate the opinion or stance of the LLM's output across a broad cluster of related queries, rather than a single specific prompt. The attack leverages a two-stage pipeline: (1) Knowledge-Guided Attack, where an LLM is used to edit a target document to…

JailbreakEdit is a novel attack that injects a universal jailbreak backdoor into safety-aligned Large Language Models (LLMs) by exploiting model editing techniques. The attack modifies specific parameters within the model's feed-forward networks, creating shortcuts that bypass internal safety mechanisms and trigger jailbroken responses to a wide range of prompts, including those containing sensitive or harmful content. The attack requires only one-time parameter modification, making it…

The SMAB (Sensitivity-based Multi-Armed Bandit) framework introduces a vulnerability in text classifiers and Large Language Models (LLMs) by enabling efficient, black-box adversarial text generation. The vulnerability exploits "word sensitivity"—the statistical probability that perturbing a specific word will flip a model's prediction—without requiring access to model weights or ground truth labels. By utilizing a Multi-Armed Bandit algorithm to explore and exploit word-level sensitivities…

The Emoti-Attack vulnerability constitutes a zero-word-perturbation adversarial attack against Natural Language Processing (NLP) systems and Large Language Models (LLMs). The vulnerability exploits the discrete embedding space of emojis and emoticons to manipulate model behavior without altering the semantic content or character integrity of the original text. By appending strategically optimized emoji sequences to the prefix and suffix of an input string (formalized as $s \oplus x \oplus…

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…

A vulnerability in several large language models (LLMs), including Qwen2.5-7BInstruct, Llama3.1-8B-Instruct, and GPT-4 variants, allows for black-box jailbreaking via prompt engineering techniques that exploit the proximity of benign and malicious prompt embeddings in the model's representation space. An attacker can craft prompts leveraging reinforcement learning to manipulate the embedding, causing the model to bypass its safety mechanisms and generate harmful or undesirable outputs while…