Language Model Security Database

Large Language Models (LLMs) employing alignment safeguards and safety mechanisms are vulnerable to graph-based adversarial attacks that bypass these protections. The attack, termed "Graph of Attacks" (GOAT), leverages a graph-based reasoning framework to iteratively refine prompts and exploit vulnerabilities more effectively than previous methods. The attack synthesizes information across multiple reasoning paths to generate human-interpretable prompts that elicit undesired or harmful outputs…

Large Language Models (LLMs) employing safety mechanisms are vulnerable to a graph-based attack that leverages semantic transformations of malicious prompts to bypass safety filters. The attack, termed GraphAttack, uses Abstract Meaning Representation (AMR), Resource Description Framework (RDF), and JSON knowledge graphs to represent malicious intent, systematically applying transformations to evade surface-level pattern recognition used by existing safety mechanisms. A particularly effective…

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) employing safety mechanisms based on supervised fine-tuning and preference alignment exhibit a vulnerability to "steering" attacks. Maliciously crafted prompts or input manipulations can exploit representation vectors within the model to either bypass censorship ("refusal-compliance vector") or suppress the model's reasoning process ("thought suppression vector"), resulting in the generation of unintended or harmful outputs. This vulnerability is demonstrated…

Large Language Model (LLM) guardrail systems, including those relying on AI-driven text classification models (e.g., fine-tuned BERT models), are vulnerable to evasion via character injection and adversarial machine learning (AML) techniques. Attackers can bypass detection by injecting Unicode characters (e.g., zero-width characters, homoglyphs) or using AML to subtly perturb prompts, maintaining semantic meaning while evading classification. This allows malicious prompts and jailbreaks to…

Multi-Agent Debate (MAD) frameworks leveraging Large Language Models (LLMs) are vulnerable to amplified jailbreak attacks. A novel structured prompt-rewriting technique exploits the iterative dialogue and role-playing dynamics of MAD, circumventing inherent safety mechanisms and significantly increasing the likelihood of generating harmful content. The attack succeeds by using narrative encapsulation, role-driven escalation, iterative refinement, and rhetorical obfuscation to guide agents…

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 contextual integrity vulnerability exists in the memory mechanisms of multi-turn Text-to-Image (T2I) generation systems (e.g., those integrating Large Language Models with diffusion models). The vulnerability, dubbed "Inception," arises because safety filters typically operate on a per-turn basis, inspecting only the current input prompt, while the image generation model operates on an aggregated context (memory) of the conversation history. An attacker can exploit this discrepancy by…

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 exists in multiple LLMs allowing attackers to elicit harmful responses by strategically distributing malicious intent across multiple turns in a conversation. The vulnerability is not detected by single-turn safety measures, as the harmful intent is only revealed through a sequence of seemingly benign prompts. The vulnerability is exacerbated by the use of techniques such as prompt optimization that dynamically adjust prompts based on model responses, maximizing the likelihood…