Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model LLM safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of...

Exploit for CVE-2025-14174

CVE-2025-14174 PoC Exploit Repository Overview This reposi...

Cisco Integrated AI Security and Safety Framework Report

Artificial intelligence AI systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and enterprise software to networked systems with embedded agents. While this has unlocked potential for human productivity gains, the attack surface has...

A Safety and Security Framework for Real-World Agentic Systems

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrator...

Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Security

As the use of large language models LLMs continues to expand, ensuring their safety and robustness has become a critical challenge. In particular, jailbreak attacks that bypass built-in safety mechanisms are increasingly recognized as a tangible threat across industries, driving the need for...

From Tabletop to Turnkey: Building Cyber Resilience in Financial Services

Introduction Financial institutions are facing a new reality: cyber-resilience has passed from being a best practice to an operational necessity to a prescriptive regulatory requirement. Crisis management or Tabletop exercises, for a long time relatively rare in the context of cybersecurity, have...

SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents Via Distilled Structured Reasoning

The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring their safe deployment. We present SIRAJ: a generic red-teaming framework for arbitrary black-box LLM agents. We emplo...

Jailbreak Mimicry: Automated Discovery of Narrative-Based Jailbreaks for Large Language Models

Large language models LLMs remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety mechanisms, posing significant risks in cybersecurity applications. We introduce Jailbreak Mimicry, a systematic methodology for training compact attacker mode...

Ask What Your Country Can Do for You: Towards a Public Red Teaming Model

AI systems have the potential to produce both benefits and harms, but without rigorous and ongoing adversarial evaluation, AI actors will struggle to assess the breadth and magnitude of the AI risk surface. Researchers from the field of systems design have developed several effective sociotechnic...

Genesis: Evolving Attack Strategies for LLM Web Agent Red-Teaming

As large language model LLM agents increasingly automate complex web tasks, they boost productivity while simultaneously introducing new security risks. However, relevant studies on web agent attacks remain limited. Existing red-teaming approaches mainly rely on manually crafted attack strategies...

CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks

Multimodal Large Language Models MLLMs achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks. While existing work focuses on explicit attacks, where malicious content resides in a single modality, recent studies reveal implicit attacks, in which...

BlueCodeAgent: A Blue Teaming Agent Enabled by Automated Red Teaming for CodeGen AI

As large language models LLMs are increasingly used for code generation, concerns over the security risks have grown substantially. Early research has primarily focused on red teaming, which aims to uncover and evaluate vulnerabilities and risks of CodeGen models. However, progress on the blue...

RedTWIZ: Diverse LLM Red Teaming Via Adaptive Attack Planning

This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models LLMs in AI-assisted software development. Our work is driven by three major research streams: 1...

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits

We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space for example, discovery requests, motions, meet-and-confer,...

RedCodeAgent: Automatic Red-Teaming Agent against Diverse Code Agents

Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic execution, debugging, and interactive programming capabilities. While these advancements have streamlined complex workflows, they have also...

Automatic Red Teaming LLM-Based Agents with Model Context Protocol Tools

The remarkable capability of large language models LLMs has led to the wide application of LLM-based agents in various domains. To standardize interactions between LLM-based agents and their environments, model context protocol MCP tools have become the de facto standard and are now widely...

RedTeam-Tactics-and-Techniques

It is an offensive tool for Windows. The repository contains a script that appears to be a Windows shell extension, likely used for red teaming or penetration testing. The script is written in a format compatible with the Windows Taskbar and Shell. The script is designed to toggle the desktop,...

AI-Powered Villager Pen Testing Tool Hits 11,000 PyPI Downloads Amid Abuse Concerns

A new artificial intelligence AI-powered penetration testing tool linked to a China-based company has attracted nearly 11,000 downloads on the Python Package Index PyPI repository, raising concerns that it could be repurposed by cybercriminals for malicious purposes. Dubbed Villager, the framewor...

offensiveinterview

It is an offensive tool for penetration testing and red teaming. The repository contains a collection of interview questions to screen offensive red team/pentest candidates, categorized into open-ended, knowledge-based, and scenario-based questions. The questions cover various topics such as...

From Firewalls to Frontiers: AI Red-Teaming Is a Domain-Specific Evolution of Cyber Red-Teaming

A red team simulates adversary attacks to help defenders find effective strategies to defend their systems in a real-world operational setting. As more enterprise systems adopt AI, red-teaming will need to evolve to address the unique vulnerabilities and risks posed by AI systems. We take the...