Language Model Security Database

Large Language Models (LLMs) are vulnerable to structure transformation attacks, where malicious prompts are encoded in diverse syntax spaces (e.g., SQL, JSON, LLM-generated syntaxes) to bypass safety mechanisms. These attacks maintain the harmful intent while altering the linguistic structure, making detection based on token-level patterns ineffective.

Large Language Models (LLMs) are vulnerable to "Rewrite to Jailbreak" (R2J) attacks. R2J exploits the models' safety mechanisms by iteratively rewriting harmful prompts, subtly altering wording to bypass safety filters while maintaining the original malicious intent. This differs from previous methods which rely on adding extraneous prefixes/suffixes or creating forced instruction-following scenarios, thus being more difficult to detect.

Large Language Models (LLMs) trained with safety fine-tuning techniques are vulnerable to multi-dimensional evasion attacks. Safety-aligned behavior, such as refusing harmful queries, is controlled not by a single direction in activation space, but by a subspace of interacting directions. Manipulating non-dominant directions, which represent distinct jailbreak patterns or indirect features, can suppress the dominant direction responsible for refusal, thereby bypassing learned safety…

A multi-turn prompt injection attack, termed "Foot-In-The-Door" (FITD), exploits the psychological principle of incremental commitment to progressively escalate malicious requests, bypassing LLM safety mechanisms. The attack leverages intermediate "bridge" prompts and self-alignment techniques to coax the model into generating increasingly harmful outputs, even when initially refusing similar direct requests.

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreaking attack leveraging a "Distraction Hypothesis". The attack, termed Contrasting Subimage Distraction Jailbreaking (CS-DJ), bypasses safety mechanisms by using multiple contrasting subimages and a decomposed harmful prompt to overwhelm the model's attention and reduce its ability to identify malicious content. The complexity of the visual input, rather than its specific content, is the key to successful exploitation.

A novel "Flanking Attack" exploits the vulnerability of multimodal LLMs (e.g., Google Gemini) to bypass content moderation filters by embedding adversarial prompts within a sequence of benign prompts. The attack leverages the LLM's processing of both audio and text, obfuscating harmful requests through contextualization and layering, thereby yielding policy-violating responses.

Large Language Models (LLMs) are vulnerable to one-shot steering vector optimization attacks. By applying gradient descent to a single training example, an attacker can generate steering vectors that induce or suppress specific behaviors across multiple inputs, even those unseen during the optimization process. This allows malicious actors to manipulate the model's output in a generalized way, bypassing safety mechanisms designed to prevent harmful responses.

Large Language Models (LLMs) with structured output interfaces are vulnerable to jailbreak attacks that exploit the interaction between token-level inference and sentence-level safety alignment. Attackers can manipulate the model's output by constructing attack patterns based on prefixes of safety refusal responses and desired harmful outputs, effectively bypassing safety mechanisms through iterative API calls and constrained decoding. This allows the generation of harmful content despite…

Large Language Models (LLMs) are vulnerable to QueryAttack, a novel jailbreak technique that leverages structured, non-natural query languages (e.g., SQL, URL formats, or other programming language constructs) to bypass safety alignment mechanisms. The attack translates malicious natural language queries into these structured formats, exploiting the LLM's ability to understand and process such languages without triggering safety filters designed for natural language prompts. The LLM then…

Large Language Models (LLMs), specifically Llama 2, Llama 3, Gemma, and Vicuna, are vulnerable to an adaptive, distributional adversarial attack methodology termed "REINFORCE." Existing gradient-based jailbreak attacks (such as Greedy Coordinate Gradient - GCG) typically optimize adversarial suffixes to maximize the likelihood of a fixed affirmative response (e.g., "Sure, here is how"). The REINFORCE method circumvents this by treating the LLM as a probabilistic policy and using Reinforcement…