Language Model Security Database

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.

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 "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 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) are vulnerable to attention-based jailbreak attacks. Attackers can craft prompts that strategically divert the LLM's attention away from sensitive words, causing the model to overlook malicious intent and generate harmful content. This occurs by leveraging the LLM's attention mechanism to focus on benign parts of the prompt while embedding harmful queries within a seemingly harmless context. The success of the attack is correlated with specific attention…

Large Language Models (LLMs) are vulnerable to jailbreaking attacks that manipulate attention scores to redirect the model's focus away from safety protocols. The AttnGCG attack method increases the attention score on adversarial suffixes within the input prompt, causing the model to prioritize the malicious content over safety guidelines, leading to the generation of harmful outputs.

Large Language Models (LLMs) are vulnerable to jailbreak attacks using autonomously discovered strategies. AutoDAN-Turbo, a black-box attack method, demonstrates the ability to discover novel and highly effective jailbreak strategies without human intervention, achieving a high success rate (e.g., 88.5% on GPT-4-1106-turbo) in eliciting harmful or unsafe responses from LLMs. The attack leverages a lifelong learning agent to iteratively refine attack strategies based on model responses…

Large Language Models (LLMs) are vulnerable to stealthy jailbreak attacks leveraging benign data mirroring. Attackers train a local "mirror model" on benign data obtained from the target LLM. This mirror model, mimicking the target's behavior, is then used to generate adversarial prompts, which are subsequently deployed against the target LLM, bypassing content moderation systems due to the lack of overtly malicious content in the initial data gathering phase.

Large Language Models (LLMs) are vulnerable to a novel "bijection learning" attack that leverages in-context learning to teach the model a custom string-to-string encoding, bypassing built-in safety mechanisms. The attack encodes harmful queries, sends them to the model, and decodes the response, effectively circumventing safety filters. The complexity of the encoding can be controlled, adapting the attack to various LLMs; more capable models are more susceptible to complex encodings.

Large Language Models (LLMs) acting as code assistants may recommend malicious code or resources when presented with prompts framed as programming challenges, even if they refuse similar direct prompts. This occurs due to insufficient context-aware safety mechanisms. LLMs may suggest compromised libraries, malicious APIs, or other attack vectors within seemingly benign code examples.