What Your Board Gets Wrong About AI Security

Editor's note: This article was originally published by Craig Riddell on LinkedIn. It has been republished here with the author's permission. Boards are giving AI security more airtime than ever. What they're not giving is the right framing. A year or two ago, AI was mostly a question of...

shadowstrike

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AI Agents May Always Fall for Prompt Injections

Prompt injection is the most critical vulnerability in deployed AI agents. Despite recent progress, we show that the prevailing defense paradigm data-instruction separation both fails to detect attacks that operate through contextual manipulation and degrades contextually appropriate behavior. We...

A Red Teaming Framework for Evaluating Robustness of AI-Enabled Security Orchestration, Automation, and Response Systems

AI-enabled Security Orchestration, Automation, and Response SOAR systems increasingly employ autonomous agents for cyber defense, yet their resilience to adaptive adversaries is underexplored. We introduce an autonomous red teaming framework that integrates large language models LLMs with...

The Machine Found It First. The Machine Will Exploit It Next.

& For decades, the question behind every CVE has been "who found it, and how fast can attackers catch up?" As of May 12, 2026, the question has flipped. Machines found the bug. Machines will weaponize the next one. The race is no longer human-versus-human with a stopwatch. Discovery Discovery...

Defense in depth for autonomous AI agents

Designing Secure Autonomous AI Agents with Defense in Depth AI agents are moving beyond assistance and into action. Instead of generating content, they invoke tools, modify data, trigger workflows, and operate across systems with increasing autonomy. This shift changes the security problem...

Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving

Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors, including biased object tracking or trajectory prediction, through i sophisticated physical patch inducing detection box drift when entering the view distance, or ii dynamically changi...

PwnedAgent

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Dark-Moon

The Open-Source AI-...

Mythos

Mythos Autonomous cybersecurity agent that connects to multip...

A Framework for AI Threat Readiness

AI models now find and exploit zero-days autonomously. This 4-pillar framework accelerates patching, analysis, and threat response...

DarkMoon - the Open-Source AI-Powered Autonomous Penetration Testing Platform

DarkMoon is an automated penetration testing tool that orchestrates complete security assessments using artificial intelligence security agents. Built as an open-source cybersecurity tool, it enables organizations to run professional-grade vulnerability assessments without manual intervention...

CVE-2026-41643

GoBGP is an open source Border Gateway Protocol BGP implementation in the Go Programming Language. Prior to version 4.3.0, a remote Denial of Service DoS vulnerability exists in GoBGP where a malformed BGP UPDATE message can trigger a runtime error: index out of range panic. This occurs during th...

groovestrike

GrooveStrike Autonomous Penetration Testing Framework...

From Specification to Deployment: Empirical Evidence from a W3C VC + DID Trust Infrastructure for Autonomous Agents

Autonomous AI agents now transact at production scale -- 69,000 bots executing 165 million transactions across 50 million USDC in cumulative volume on a single marketplace -- without any shared trust layer between participants. Regulatory frameworks Singapore IMDA, NIST CAISI, EU AI Act and major...

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DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents

AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-world incidents have...

Autonomous LLM Agent Worms: Cross-Platform Propagation, Automated Discovery and Temporal Re-Entry Defense

Autonomous LLM agents operate as long-running processes with persistent workspaces, memory files, scheduled task state, and messaging integrations. These features create a new propagation risk: attacker-influenced content can be written into persistent agent state, re-enter the LLM decision conte...

Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense

Agentic systems involved in high-stake decision-making under adversarial pressure need formal guarantees not offered by existing approaches. Motivated by the operational needs of security operations centers SOCs that must configure endpoint detection and response EDR policies under adversarial...