The Wire.Tracking threats to Agents 312 raw → 45 curated · updated 27 Jun 2026

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What happened after 2,000 people tried to hack my AI assistant

Fernando Irarrázaval ran a public challenge at hackmyclaw.com inviting people to leak secrets from his OpenClaw test instance via email-based prompt injection. After roughly 6,000 attempts by ~2,000 people, nobody succeeded in extracting the secret, with the instance protected by anti-prompt-injection system rules on the underlying model.

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Dawn of the Apex Agentic Adversary

An analysis piece arguing that autonomous, agentic AI adversaries are compressing the timeline of cyberattacks beyond human-speed defenses, ending the era of human-paced threat cycles. The available text is introductory commentary without specific technical proof-of-concept details.

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Forget Data Leakage: Shadow AI's Real Threat Is Access Control

The article argues that shadow AI in enterprises has evolved from a data leakage concern into an access control problem, where the risk lies in autonomous AI tools and agents having unmanaged access permissions rather than just employees pasting sensitive data.

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MCP Supply Chain Attacks: Why Better Models Make It Worse

An analysis arguing that the Model Context Protocol (MCP) is following the insecure early-API trajectory by leaving authentication, authorization, input validation, and sandboxing to implementers. It highlights that compromising the AI/MCP layer can cause broader, harder-to-trace damage than a compromised API because LLM-driven agents autonomously select tools and can be manipulated via upstream data or prompts.

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Quoting Matteo Wong, The Atlantic

An Atlantic piece quotes cybersecurity expert Katie Moussouris discussing a White House report on a Claude jailbreak, where the model refused to 'review code for security issues' but complied when asked to 'fix this code.' Moussouris characterized this as the model working as intended for cyberdefense rather than a genuine exploit.

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Is security a skill issue? Five scanners, 3,084 skills, a different verdict 64% of the time · Mastro

A Mastro study analyzed 3,084 agent skills across five security scanners and found they disagree on a verdict 63.9% of the time, with 14.2% rated CRITICAL by one scanner and SAFE by another. The piece frames the broader supply-chain risk of AI agent skills—markdown files agents execute with full tool access—citing reported incidents where malicious skills lifted SSH keys, cloud credentials, and crypto wallets, and a fake download counter pushed a dummy skill to #1.

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GitHub - denoland/clawpatrol: Security firewall for agents · GitHub

Clawpatrol is an open-source security firewall for AI agents from denoland, designed to sandbox external plugins (treated as an untrusted supply-chain attack surface) using OS-level namespaces, Landlock, and macOS sandbox profiles, with permission lockfiles and brokered network dialing.

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Polymarket annotation injection

The author found injected annotations on a Polymarket event page that are rendered server-side and therefore visible to LLMs via web_search even when hidden in the browser. A planted annotation (source 'grok') contained a fake emergency-rate-cut message directing users to withdraw funds at a phishing-style domain, representing an indirect prompt-injection vector through Polymarket's annotation API endpoints. Claude's web search saw the content but correctly flagged it as phishing.

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GitHub - pixiebrix/agent-browser-shield: Browser extension with 35+ rules for keeping your AI agent safe while browsing · GitHub

A GitHub repository for 'agent-browser-shield,' a browser extension by pixiebrix offering 35+ rules aimed at keeping AI agents safe while browsing. It is a defensive tool addressing risks to browser-based AI agents rather than a report of a specific threat.

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Inside MCP: defending the runtime layer of agent security · Arcis Blog

An Arcis blog post argues that agent security has four layers (identity, pre-deploy testing, observability, runtime defense) and that the runtime hot path is structurally underserved. It frames MCP's explicit tool-call contract as enabling runtime defense against agent toolcall injection (their vector V32), applying allowlist/sanitize/refuse techniques at the agent-tool boundary.

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