Language Model Security Database

LLMStinger uses a reinforcement-learning loop to fine-tune an attacker model that generates adversarial suffixes for jailbreak prompts. Because the approach does not require white-box access to the target, it can adapt existing attacks for both open- and closed-source assistants and bypass otherwise effective refusal behavior.

A vulnerability in multi-modal large language models (MLLMs) allows attackers to bypass safety mechanisms and elicit harmful responses using a memory-efficient zeroth-order optimization technique. The attack, termed Zer0-Jack, leverages simultaneous perturbation stochastic approximation (SPSA) with patch coordinate descent to generate malicious image inputs, even without access to the model's internal parameters (black-box setting).

Large Language Models (LLMs) are vulnerable to "SequentialBreak," a jailbreak attack where embedding a harmful prompt within a chain of benign prompts in a single query can bypass LLM safety features. The LLM's attention mechanism prioritizes the benign prompts, allowing the harmful prompt to be processed without triggering safety mitigations.

Large Language Models (LLMs) are vulnerable to a jailbreak attack that leverages the model's ability to generate diverse and obfuscated prompts to bypass safety constraints. The attack exploits the model's capacity to deviate from prior context, rendering existing safety training ineffective. The attacker uses a multi-stage process involving diversification (generating prompts significantly different from previous attempts) and obfuscation (obscuring sensitive words/phrases) to elicit harmful…

Large Language Models (LLMs) are vulnerable to multi-round jailbreak attacks which leverage a heuristic search process to progressively elicit harmful content. The attack decomposes a harmful query into multiple, seemingly innocuous sub-queries, iteratively refining the prompts based on the LLM's responses and employing psychological strategies to bypass safety mechanisms. This allows for the circumvention of single-round detection methods and elicitation of responses containing prohibited…

Large Language Models (LLMs) employing safety alignment mechanisms are vulnerable to a bypass attack using simple, stochastic random augmentations of input prompts. The attack leverages the inherent brittleness of safety alignment to minor, randomly introduced modifications in the input, causing the LLM to generate unsafe outputs despite its safety training. Character-level augmentations prove significantly more effective than string insertions.

A poisoning attack against a Retrieval-Augmented Generation (RAG) system that manipulates the retriever component by injecting a poisoned document into the data used by the embedding model. This poisoned document contains modified and incorrect information. When activated, the system retrieves the poisoned document and uses it to generate misleading, biased, and unfaithful responses to user queries.

A novel SQL Injection Jailbreak (SIJ) vulnerability allows attackers to bypass safety mechanisms in Large Language Models (LLMs) by manipulating the structure of input prompts. The attack leverages the model's processing of system prompts, user prefixes, user prompts, and assistant prefixes to effectively "comment out" the expected response prefix and inject harmful instructions, causing the LLM to generate unsafe content. This vulnerability exploits the external properties of the LLM…

Large language models (LLMs) are vulnerable to jailbreak attacks exploiting nonlinear features within prompt encodings. These features, not detectable by linear methods, allow adversaries to reliably elicit harmful outputs despite safety training. Different attack methods leverage distinct nonlinear features, limiting the transferability of detection and mitigation techniques.

Large Language Models (LLMs) are vulnerable to jailbreaking attacks using sequences of invertible string transformations (string compositions). Attackers can combine multiple transformations (e.g., leetspeak, Base64, ROT13, word reversal) to obfuscate malicious prompts, bypassing safety mechanisms that detect simpler attacks. Even with safety training, the models fail to correctly interpret the transformed input and produce unsafe outputs.