Language Model Security Database

Large Language Models (LLMs) exhibit a bias towards authoritative sources, allowing attackers to bypass safety mechanisms by crafting prompts that include fabricated citations mimicking credible sources (e.g., research papers, GitHub repositories). The model's trust in these fabricated citations leads to the generation of harmful content.

A vulnerability exists in several Large Vision-Language Models (LVLMs) where seemingly safe images, when combined with additional safe images and prompts using a specific attack methodology (Safety Snowball Agent), can trigger the generation of unsafe and harmful content. The vulnerability exploits the models' universal reasoning abilities and a "safety snowball effect," where an initial unsafe response leads to progressively more harmful outputs.

Large Language Models (LLMs) are vulnerable to jailbreak attacks using language games, which manipulate input prompts through structured linguistic alterations (e.g., Ubbi Dubbi, custom letter insertion rules) to bypass safety mechanisms. These games obfuscate malicious intent while maintaining human readability, causing LLMs to generate unsafe content.

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 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…