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AARTF---Autonomous-AI-RedTeam-Framework

AARTF AI-Driven Autonomous Security Workflow !CIhttps:/...

Addressing the OWASP Top 10 Risks in Agentic AI with Microsoft Copilot Studio

Agentic AI is moving fast from pilots to production. That shift changes the security conversation. These systems do not just generate content. They can retrieve sensitive data, invoke tools, and take action using real identities and permissions. When something goes wrong, the failure is not limit...

Design Principles for the Construction of a Benchmark Evaluating Security Operation Capabilities of Multi-Agent AI Systems

As Large Language Models LLMs and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations, policymakers, model providers, and researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such AI systems to...

The Kill Chain Is Obsolete When Your AI Agent Is the Threat

In September 2025, Anthropic disclosed that a state-sponsored threat actor used an AI coding agent to execute an autonomous cyber espionage campaign against 30 global targets. The AI handled 80-90% of tactical operations on its own, performing reconnaissance, writing exploit code, and attempting...

Policy-Guided Threat Hunting: An LLM Enabled Framework with Splunk SOC Triage

With frequently evolving Advanced Persistent Threats APTs in cyberspace, traditional security solutions approaches have become inadequate for threat hunting for organizations. Moreover, SOC Security Operation Centers analysts are often overwhelmed and struggle to analyze the huge volume of logs...

TreeTeaming: Autonomous Red-Teaming of Vision-Language Models Via Hierarchical Strategy Exploration

The rapid advancement of Vision-Language Models VLMs has brought their safety vulnerabilities into sharp focus. However, existing red teaming methods are fundamentally constrained by an inherent linear exploration paradigm, confining them to optimizing within a predefined strategy set and...

Farming at the Edge: Where Autonomous Robots and Edge Compute Meet

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STRIATUM-CTF: A Protocol-Driven Agentic Framework for General-Purpose CTF Solving

Large Language Models LLMs have demonstrated potential in code generation, yet they struggle with the multi-step, stateful reasoning required for offensive cybersecurity operations. Existing research often relies on static benchmarks that fail to capture the dynamic nature of real-world...

AI in Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes

Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validated competition designs and evaluation practices that remain fair and evidence-based. This paper presents a...

DeepXplain: XAI-Guided Autonomous Defense against Multi-Stage APT Campaigns

Advanced Persistent Threats APTs are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning DRL enables autonomous cyber defense, its decisions are often opaque and difficult to trust in operational environments. This paper presents DeepXplain, a...

T-MAP: Red-Teaming LLM Agents with Trajectory-Aware Evolutionary Search

While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models LLMs, such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protoc...

Secure agentic AI end-to-end

Next week, RSAC™ Conference celebrates its 35-year anniversary as a forum that brings the security community together to address new challenges and embrace opportunities in our quest to make the world a safer place for all. As we look towards that milestone, agentic AI is reshaping industries...

Pensar Apex AI-Powered Penetration Testing

Pensar Apex is an AI-powered penetration testing using autonomous agents - directly in your terminal. Run blackbox and whitebox pentests that explore, reason, and surface real vulnerabilities...

Exploit for OS Command Injection in Php

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Measuring and Exploiting Confirmation Bias in LLM-Assisted Security Code Review

Security code reviews increasingly rely on systems integrating Large Language Models LLMs, ranging from interactive assistants to autonomous agents in CI/CD pipelines. We study whether confirmation bias i.e., the tendency to favor interpretations that align with prior expectations affects LLM-bas...

Why Security Validation Is Becoming Agentic

If you run security at any reasonably complex organization, your validation stack probably looks something like this: a BAS tool in one corner. A pentest engagement, or maybe an automated pentesting product, in another. A vulnerability scanner feeding an attack surface management platform somewhe...

ClawWorm: Self-Propagating Attacks across LLM Agent Ecosystems

Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplored. In particular, OpenClaw, an open-source platform with over 40,000 active instances, has stood out recently with it...

Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw

The rapid evolution of Large Language Models LLMs into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape. Frameworks like OpenClaw grant AI systems operating-system-level permissions and the autonomy to execute complex workflows. This level of access creates...

Securing Autonomous AI Agents with TrendAI & NVIDIA OpenShell

Learn how TrendAI and NVIDIA OpenShell help secure autonomous AI agents and build trusted enterprise AI systems with stronger visibility and control...