Prompt layer Vulnerabilities

A vulnerability exists in Large Language Model (LLM) decision-making capabilities described as "Rhetorical Persuasion Override." When an LLM is deployed as a judge or evaluator in a single-turn, multi-agent debate framework, it systematically fails to distinguish factual truth from confidently presented misinformation. An adversarial agent can coerce the evaluator into endorsing a known falsehood from the TruthfulQA dataset by employing specific rhetorical strategies—namely, high confidence…

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…

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…

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) 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.

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…

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…