Practical Reasoning Interruption Attacks on Reasoning Large Language Models

Reasoning large language models RLLMs have demonstrated outstanding performance across a variety of tasks, yet they also expose numerous security vulnerabilities. Most of these vulnerabilities have centered on the generation of unsafe content. However, recent work has identified a distinct...

Exploring PLeak: An Algorithmic Method for System Prompt Leakage

What is PLeak, and what are the risks associated with it? We explored this algorithmic technique and how it can be used to jailbreak LLMs, which could be leveraged by threat actors to manipulate systems and steal sensitive data...

XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs

Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adoption in critical application scenarios such as government organizations and medical institutions. For this reason,...

New Reports Uncover Jailbreaks, Unsafe Code, and Data Theft Risks in Leading AI Systems

Various generative artificial intelligence GenAI services have been found vulnerable to two types of jailbreak attacks that make it possible to produce illicit or dangerous content. The first of the two techniques, codenamed Inception, instructs an AI tool to imagine a fictitious scenario, which...

Inception: Jailbreak the Memory Mechanism of Text-To-Image Generation Systems

Currently, the memory mechanism has been widely and successfully exploited in online text-to-image T2I generation systems e.g., DALL E 3 for alleviating the growing tokenization burden and capturing key information in multi-turn interactions. Despite its practicality, its security analyses have...

Prefill-Based Jailbreak: a Novel Approach of Bypassing LLM Safety Boundary

Large Language Models LLMs are designed to generate helpful and safe content. However, adversarial attacks, commonly referred to as jailbreak, can bypass their safety protocols, prompting LLMs to generate harmful content or reveal sensitive data. Consequently, investigating jailbreak methodologie...

JailbreaksOverTime: Detecting Jailbreak Attacks under Distribution Shift

Safety and security remain critical concerns in AI deployment. Despite safety training through reinforcement learning with human feedback RLHF 32, language models remain vulnerable to jailbreak attacks that bypass safety guardrails. Universal jailbreaks - prefixes that can circumvent alignment fo...

Graph of Attacks: Improved Black-Box and Interpretable Jailbreaks for LLMs

The challenge of ensuring Large Language Models LLMs align with societal standards is of increasing interest, as these models are still prone to adversarial jailbreaks that bypass their safety mechanisms. Identifying these vulnerabilities is crucial for enhancing the robustness of LLMs against su...

Various GPT services are vulnerable to two systemic jailbreaks, allows for bypass of safety guardrails

Overview Two systemic jailbreaks, affecting a number of generative AI services, were discovered. These jailbreaks can result in the bypass of safety protocols and allow an attacker to instruct the corresponding LLM to provide illicit or dangerous content. The first jailbreak, called “Inception,” ...

Amplified Vulnerabilities: Structured Jailbreak Attacks on LLM-Based Multi-Agent Debate

Multi-Agent Debate MAD, leveraging collaborative interactions among Large Language Models LLMs, aim to enhance reasoning capabilities in complex tasks. However, the security implications of their iterative dialogues and role-playing characteristics, particularly susceptibility to jailbreak attack...

GraphAttack: Exploiting Representational Blindspots in LLM Safety Mechanisms

Large Language Models LLMs have been equipped with safety mechanisms to prevent harmful outputs, but these guardrails can often be bypassed through "jailbreak" prompts. This paper introduces a novel graph-based approach to systematically generate jailbreak prompts through semantic transformations...

Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails

Large Language Models LLMs guardrail systems are designed to protect against prompt injection and jailbreak attacks. However, they remain vulnerable to evasion techniques. We demonstrate two approaches for bypassing LLM prompt injection and jailbreak detection systems via traditional character...

Token-Level Constraint Boundary Search for Jailbreaking Text-To-Image Models

Recent advancements in Text-to-Image T2I generation have significantly enhanced the realism and creativity of generated images. However, such powerful generative capabilities pose risks related to the production of inappropriate or harmful content. Existing defense mechanisms, including prompt...

Concept Enhancement Engineering: a Lightweight and Efficient Robust Defense against Jailbreak Attacks in Embodied AI

Embodied Intelligence EI systems integrated with large language models LLMs face significant security risks, particularly from jailbreak attacks that manipulate models into generating harmful outputs or executing unsafe physical actions. Traditional defense strategies, such as input filtering and...

Mesop Class Pollution vulnerability leads to DoS and Jailbreak attacks

From @jackfromeast and @superboy-zjc: We have identified a class pollution vulnerability in Mesop = 0.14.0 application that allows attackers to overwrite global variables and class attributes in certain Mesop modules during runtime. This vulnerability could directly lead to a denial of service Do...

GHSA-F3MF-HM6V-JFHH Mesop Class Pollution vulnerability leads to DoS and Jailbreak attacks

From @jackfromeast and @superboy-zjc: We have identified a class pollution vulnerability in Mesop = 0.14.0 application that allows attackers to overwrite global variables and class attributes in certain Mesop modules during runtime. This vulnerability could directly lead to a denial of service Do...

CVE-2025-30358

Mesop is a Python-based UI framework. A class pollution vulnerability in Mesop before 0.14.1 allows attackers to overwrite global variables and class attributes at runtime in certain modules, enabling DoS on the server and potential identity confusion (e.g., impersonating assistants or system rol...

CVE-2025-30358 Mesop Class Pollution vulnerability leads to DoS and Jailbreak attacks

Mesop is a Python-based UI framework that allows users to build web applications. A class pollution vulnerability in Mesop prior to version 0.14.1 allows attackers to overwrite global variables and class attributes in certain Mesop modules during runtime. This vulnerability could directly lead to...

CVE-2025-30358 Mesop Class Pollution vulnerability leads to DoS and Jailbreak attacks

Mesop is a Python-based UI framework that allows users to build web applications. A class pollution vulnerability in Mesop prior to version 0.14.1 allows attackers to overwrite global variables and class attributes in certain Mesop modules during runtime. This vulnerability could directly lead to...

Researchers Use AI Jailbreak on Top LLMs to Create Chrome Infostealer

New Immersive World LLM jailbreak lets anyone create malware with GenAI. Discover how Cato Networks researchers tricked ChatGPT, Copilot, and DeepSeek into coding infostealers - In this case, a Chrome infostealer...