Language Model Security Database

Large Language Models (LLMs) are vulnerable to adversarial prompts generated by the Rainbow Teaming technique. Rainbow Teaming uses a quality-diversity search algorithm to create a diverse set of prompts that elicit unsafe, biased, or incorrect outputs from the target LLM, exceeding a 90% success rate across various models. The vulnerability stems from the LLMs' susceptibility to these carefully crafted prompts, bypassing existing safety mechanisms. These prompts are highly transferable across…

A vulnerability exists in several Large Language Models (LLMs) allowing evasion of safety filters through carefully crafted prompts leveraging role-playing scenarios. The vulnerability is exploited by prompting the LLM to adopt a specific persona or scenario (e.g., "You are a helpful assistant in a fantasy world where all actions are permitted") that overrides built-in safety restrictions, resulting in the generation of unsafe or undesirable outputs. The attack is facilitated by structured…

Large Language Models (LLMs) are vulnerable to a novel semantic mirror jailbreak attack. This attack leverages a genetic algorithm to generate jailbreak prompts that are semantically similar to benign prompts, evading defenses based on semantic similarity metrics. The attack achieves this by optimizing for both semantic similarity to the original question and the ability to elicit harmful responses.

Large Language Models (LLMs) are vulnerable to a novel attack leveraging subconscious exploitation and echopraxia. Attackers craft prompts that subtly guide the LLM to echo malicious content it has implicitly learned during pre-training but is programmed to suppress. This bypasses safety mechanisms designed to prevent the generation of harmful content. The technique involves extracting malicious knowledge from the LLM's conditional probability distribution (representing its "subconscious") and…

A novel attack, dubbed PRP (Propagating Universal Perturbations), bypasses guardrail LLMs by constructing a universal adversarial prefix that, when prepended to any harmful response, evades detection by the guard model. This prefix is then propagated to the base LLM's response using in-context learning, causing the guardrail LLM to generate harmful content.

Large Language Models (LLMs) used for zero-shot text assessment are vulnerable to universal adversarial attacks. Concatenating short phrases ("universal adversarial phrases") to assessed text can artificially inflate the predicted scores, regardless of the actual quality of the text. This vulnerability is particularly pronounced in LLMs performing absolute scoring, as opposed to comparative assessment.

Large language models (LLMs) are vulnerable to jailbreaking attacks that exploit human-like persuasive techniques rather than algorithmic or technical flaws. Attackers can craft prompts ("Persuasive Adversarial Prompts" or PAPs) leveraging social influence strategies (e.g., logical appeal, emotional appeal, authority endorsement) to elicit responses that violate safety guidelines and reveal sensitive or harmful information. The effectiveness of these attacks surpasses traditional…

A vulnerability in the safety alignment of large language models (LLMs) allows a "weak-to-strong" jailbreaking attack. This attack uses a smaller, adversarially trained ("unsafe") LLM to manipulate the decoding probabilities of a much larger, safety-aligned ("safe") LLM, leading the larger model to generate harmful outputs. The attack leverages the observation that the initial decoding distributions of safe and unsafe LLMs differ significantly, but this difference diminishes as the generation…

Large Language Models (LLMs) used for code generation are vulnerable to adversarial natural language instructions that preserve semantic meaning but induce the generation of functionally correct code containing specific vulnerabilities. The attack leverages a novel algorithm, DeceptPrompt, to generate adversarial prompts that manipulate the LLM's output, resulting in vulnerable code without altering the intended functionality.

A vulnerability exists in large language models (LLMs) allowing for the injection of persistent backdoors via fine-tuning with a crafted dataset. The backdoor triggers the LLM to generate unsafe outputs for specific harmful prompts, while remaining undetected during standard safety audits due to the trigger's design and the backdoor's persistence against re-alignment techniques. The attack leverages elongated triggers, unlike previous attacks which used shorter triggers easily removed via…