Language Model Security Database

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…

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…

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…

The GAP framework, as described in arXiv:2501.18638, reveals vulnerabilities in various large language models (LLMs) by generating stealthy jailbreak prompts that bypass content moderation systems. The framework leverages a graph-based attack strategy, enabling knowledge sharing across attack paths for enhanced efficiency and evasion. This allows the successful bypassing of multiple LLM safety mechanisms, including those based on perplexity and prompt-based heuristics.

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

Large language models (LLMs) exhibit increased responsiveness to prompts framed within positive narratives. The Happy Ending Attack (HEA) exploits this by embedding malicious requests within a positive-sentiment scenario culminating in a happy ending. This allows the LLM to generate responses that fulfill the malicious request while perceiving the overall prompt as benign.

Large Language Models (LLMs) used in hate speech detection systems are vulnerable to adversarial attacks and model stealing, resulting in evasion of hate speech detection. Adversarial attacks modify hate speech text to evade detection, while model stealing creates surrogate models that mimic the target system's behavior.