Language Model Security Database

Many-Shot Jailbreaking (MSJ) is an adversarial technique that circumvents the safety alignment of Large Language Models (LLMs) by exploiting their In-Context Learning (ICL) capabilities and extended context windows. By embedding a large number of "shots" (fake dialogue examples) within a single prompt—where a simulated assistant complies with harmful requests—the attacker conditions the model to ignore its safety training. As the number of malicious examples increases (following a power-law…

A vulnerability in Large Language Models (LLMs) allows attackers to bypass safety mechanisms and elicit detailed harmful responses by strategically manipulating input prompts. The vulnerability exploits the LLM's sensitivity to "scenario shifts"—contextual changes in the input that influence the model's output, even when the core malicious request remains the same. A genetic algorithm can optimize these scenario shifts, increasing the likelihood of obtaining detailed harmful responses while…

Large Language Models (LLMs) are vulnerable to a jailbreaking attack leveraging humorous prompts. Embedding an unsafe request within a humorous context, using a fixed template, bypasses built-in safety mechanisms and elicits unsafe responses. The attack's success relies on a balance; too little or too much humor reduces effectiveness.

Large Language Models (LLMs) exhibit Defense Threshold Decay (DTD): generating substantial benign content shifts the model's attention from the input prompt to prior outputs, increasing susceptibility to jailbreak attacks. The "Sugar-Coated Poison" (SCP) attack exploits this by first generating benign content, then transitioning to malicious output.

State-of-the-art Reward Models (RMs) utilized in Reinforcement Learning from Human Feedback (RLHF) exhibit poor out-of-distribution (OOD) generalization, making them susceptible to adversarial inputs. These models fail to reliably assess prompt-response pairs that diverge from their training distribution, assigning high reward scores to low-quality, nonsensical, or syntactically incorrect responses. This vulnerability allows for "reward hacking," where a policy model optimizes for unintended…

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreaking attack, dubbed PiCo, that leverages token-level typographic attacks on images embedded within code-style instructions. The attack bypasses multi-tiered defense mechanisms, including input filtering and runtime monitoring, by exploiting weaknesses in the visual modality's integration with programming contexts. Harmful intent is concealed within visually benign image fragments and code instructions, circumventing safety…

Multilingual and multi-accent audio inputs, combined with acoustic adversarial perturbations (reverberation, echo, whisper effects), can bypass safety mechanisms in Large Audio Language Models (LALMs), causing them to generate unsafe or harmful outputs. The vulnerability is amplified by the interaction between acoustic and linguistic variations, particularly in languages with less training data.

A vulnerability in multi-agent Large Language Model (LLM) systems allows for a permutation-invariant adversarial prompt attack. By strategically partitioning adversarial prompts and routing them through a network topology, an attacker can bypass distributed safety mechanisms, even those with token bandwidth limitations and asynchronous message delivery. The attack optimizes prompt propagation as a maximum-flow minimum-cost problem, maximizing success while minimizing detection.

Large Language Models (LLMs) with structured output APIs (e.g., using JSON Schema) are vulnerable to Constrained Decoding Attacks (CDAs). CDAs exploit the control plane of the LLM's decoding process by embedding malicious intent within the schema-level grammar rules, bypassing safety mechanisms that primarily focus on input prompts. The attack manipulates the allowed output space, forcing the LLM to generate harmful content despite a benign input prompt. One instance of a CDA is the Chain Enum…

Large Language Models (LLMs) incorporating safety filters are vulnerable to a "Prompt, Divide, and Conquer" attack. This attack segments a malicious prompt into smaller, seemingly benign parts, processes these segments in parallel across multiple LLMs, and then reassembles the results to generate malicious code, bypassing the safety filters. The attack's success relies on the iterative refinement of initially abstract function descriptions into concrete implementations. Individual LLM safety…