Language Model Security Database

Improper Input Validation in Large Language Model (LLM) systems configured as automated evaluators ("LLM-as-a-judge") allows remote attackers to manipulate evaluation scores and comparative verdicts via adversarial prompt injection. The vulnerability arises when the model processes untrusted input containing linguistic masquerading, context separators, and disruptor commands (e.g., "Basic Injection", "Contextual Misdirection", and "Adaptive Search-Based Attack"). Successful exploitation…

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…

A vulnerability exists in the combination of Large Language Models (LLMs) and their associated safety guardrails, allowing attackers to bypass both defenses and elicit harmful or unintended outputs from LLMs. The vulnerability stems from insufficient detection by guardrails against adversarially crafted prompts, which appear benign but contain hidden malicious intent. The attack, dubbed "DualBreach," leverages a target-driven initialization strategy and multi-target optimization to generate…

Large Language Model (LLM) agents operating in stateful environments (web browsers, operating systems, and tool-use contexts) are vulnerable to indirect prompt injection and multi-modal adversarial attacks. These vulnerabilities arise when agents process untrusted environmental observations—such as web accessibility trees, screen screenshots, or database query results—that contain concealed malicious instructions. Specifically, attackers can embed prompt injections into HTML accessibility…

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…

LLM-powered GUI agents utilizing screenshot-based interpretation (such as those powered by GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, and DeepSeek V3 0324) are vulnerable to Fine-Print Injection (FPI) and Deceptive Default (DD) attacks due to a lack of visual saliency filtering. Unlike human users who prioritize prominent UI elements, these agents perform "indiscriminate parsing," processing low-salience text (e.g., privacy policies, terms of service, footer disclaimers) with the same…

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…

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…

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…