Language Model Security Database

Large Language Models (LLMs) are vulnerable to multi-step "moralized" jailbreak prompts that bypass their safety guardrails. These prompts, while appearing ethical individually, cumulatively create a context that elicits verbally aggressive and harmful content generation. The attack leverages the LLMs' inability to fully understand the cumulative context and intent across multiple prompts.

Large Language Models (LLMs) are vulnerable to a novel prompt injection attack using universal and context-independent triggers. These triggers, once discovered for a specific LLM, allow precise control over the model's output regardless of the prompt context or desired output content, enabling adversaries to force the generation of arbitrary text. The attack utilizes a gradient-based optimization technique to discover these triggers.

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.

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…

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.