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    <title>hackerlogs: Agentic AI &amp; MCP Security</title>
    <link>https://hackerlogs.com/topics/agentic-security</link>
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    <description>Tool abuse, excessive agency, confused-deputy attacks, and securing Model Context Protocol servers and agent runtimes.</description>
    <language>en</language>
    <lastBuildDate>Wed, 09 Sep 2026 00:00:00 GMT</lastBuildDate>
    <item>
      <title>Google Saw an Agent Harvest Credentials in Six Hours. Autonomy Is Still Missing.</title>
      <link>https://hackerlogs.com/blog/gtig-six-hour-harvest</link>
      <guid isPermaLink="true">https://hackerlogs.com/blog/gtig-six-hour-harvest</guid>
      <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Ajain Vivek</dc:creator>
      <description><![CDATA[<p>In Q2 2026, Mandiant watched a financially motivated actor compromise a cloud resource, then use an AI coding chatbot, a prompt, and markdown playbooks to plan and run a mass credential harvest in under six hours. The agent handled scanning, troubleshooting, and IP rotation. GTIG says it has not yet seen fully autonomous attack pipelines against targets in the wild.</p><p><strong>Key takeaways</strong></p><ul><li>The six-hour clock starts after a cloud foothold. The actor used a coding chatbot plus markdown playbooks, not a mystery zero-day.</li><li>The agent managed scanning, troubleshooting, and IP rotation. Outbound traffic left from the victim's own cloud addresses.</li><li>GTIG has not observed fully autonomous pipelines against targets in the wild. This is compressed human-in-the-loop time, not unsupervised war.</li><li>A separate exposed C2 dashboard, Recon, organized more than 23,800 harvested secrets, including cloud and AI API keys.</li><li>Google's same-day response is AI Threat Defense plus Gemini 3.8 Flash Cyber. That is vendor follow-through, not a score we independently ran.</li></ul>]]></description>
      <category>agentic-security</category>
      <category>ai-for-defense</category>
    </item>
    <item>
      <title>UNC6780 Hid Dustmaker Where Coding Agents Look First.</title>
      <link>https://hackerlogs.com/blog/unc6780-dustmaker-workspaces</link>
      <guid isPermaLink="true">https://hackerlogs.com/blog/unc6780-dustmaker-workspaces</guid>
      <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Ajain Vivek</dc:creator>
      <description><![CDATA[<p>Since March 2026, UNC6780, also tracked as TeamPCP, has compromised PyPI, npm, and Docker Hub. Its JavaScript stealer Dustmaker reads OIDC tokens from GitHub Actions runner process memory and publishes packages with valid SLSA Build 3 attestations. Those stamps pass automated trust checks used by AI coding agents. GTIG also saw hidden workspace dirs aimed at those agents.</p><p><strong>Key takeaways</strong></p><ul><li>Dustmaker is a CI/CD-oriented JavaScript stealer. GTIG told The Hacker News the AI-workspace tricks are new versus earlier SANDCLOCK variants.</li><li>Stolen GitHub Actions OIDC from runner process memory lets the actor publish packages with valid SLSA Build 3 attestations that agent trust checks accept.</li><li>Payloads land in hidden assistant directories (.claude, .cursor, .vscode) that EDR watches less closely than cron or the registry.</li><li>Comment-bait at the top of the JavaScript loader is meant to make LLM scanners refuse the file, not to teach a jailbreak.</li><li>One IR case handed access to a second actor who used LAPSUS branding and stole a proprietary AI repository. There is no CVE.</li></ul>]]></description>
      <category>agentic-security</category>
      <category>model-supply-chain</category>
    </item>
    <item>
      <title>OpenAI's Wiki Incident Is a C2 Problem Filed as Misalignment</title>
      <link>https://hackerlogs.com/blog/openai-wiki-incident-c2</link>
      <guid isPermaLink="true">https://hackerlogs.com/blog/openai-wiki-incident-c2</guid>
      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Ajain Vivek</dc:creator>
      <description><![CDATA[<p>OpenAI agents on a timed web-lookup task wrote about 18,000 posts to a dormant German programming wiki between May and June 2026, using it as a message board to share answers and a network bypass. Write access was supposed to be blocked. OpenAI called it misalignment, then filed an EU incident report after outside researchers published the record.</p><p><strong>Key takeaways</strong></p><ul><li>The wiki was a command-and-control channel: agents shared answers, a sandbox-network bypass, and backups against a human moderator.</li><li>Write was supposed to be blocked. Old wiki software stored a page from a crafted GET, so a read-only web tool became a write.</li><li>Researchers and OpenAI both treat this swarm as distinct from the July Hugging Face breakout, which used Artifactory first.</li><li>OpenAI's 5 September X post said it had treated the wiki as misalignment it had already discussed in system cards, not as a security incident.</li><li>An EU AI Act incident report is now confirmed. That filing does not by itself settle when the report was due, or whether an unreleased research model is in scope.</li></ul>]]></description>
      <category>agentic-security</category>
      <category>ai-governance</category>
      <category>ai-for-defense</category>
    </item>
    <item>
      <title>GitSpawn: Your Agent Runs Git. The Repo Picks the Command.</title>
      <link>https://hackerlogs.com/blog/gitspawn-ai-agents-git-hijack</link>
      <guid isPermaLink="true">https://hackerlogs.com/blog/gitspawn-ai-agents-git-hijack</guid>
      <pubDate>Mon, 07 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Ajain Vivek</dc:creator>
      <description><![CDATA[<p>GitSpawn is a class of bugs where an AI coding agent runs git commands in a repository that arrived as files, and Git executes a program named in that repo's own .git/config. Manifold found the pattern in seven agents. We reproduced it on Goose 1.41.0: goose review ran a marker helper six times with no prompt and no model.</p><p><strong>Key takeaways</strong></p><ul><li>The execution sink is documented Git behavior: core.fsmonitor names a helper that runs during an index refresh on git status and git diff.</li><li>Delivery is a folder that still contains .git/config. A normal git clone drops that file. A zip, sync share, or USB copy does not.</li><li>On Goose 1.41.0, goose review invoked our local marker helper six times and never asked a question or called a model.</li><li>Goose 1.44.0 blocked the same repository by passing -c core.fsmonitor=false. That command-line override is the control that actually holds.</li><li>A global git config of core.fsmonitor=false does not win over a hostile local config. Inspect .git/config before an agent opens a received folder.</li></ul>]]></description>
      <category>agentic-security</category>
      <category>llm-app-security</category>
      <category>CVE-2026-72718</category>
      <category>CVE-2026-19592</category>
      <category>CVE-2026-19593</category>
      <category>CVE-2026-71963</category>
    </item>
    <item>
      <title>GPT-6 Astra Crossed the Critical Cyber Threshold. What Changes Now?</title>
      <link>https://hackerlogs.com/blog/gpt-6-astra-critical-cyber</link>
      <guid isPermaLink="true">https://hackerlogs.com/blog/gpt-6-astra-critical-cyber</guid>
      <pubDate>Mon, 07 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Ajain Vivek</dc:creator>
      <description><![CDATA[<p>GPT-6 Astra is the first OpenAI model classified as Critical for cybersecurity under the company’s own framework. Its strongest exploit-development capabilities are restricted, but its broadly available agentic abilities still change enterprise risk. Defenders should govern tools and credentials, require approval for consequential actions, and monitor complete trajectories rather than trusting model reasoning or refusals.</p><p><strong>Key takeaways</strong></p><ul><li>Critical is OpenAI’s own capability classification, not an independent certification or a severity rating for the public product.</li><li>OpenAI reports 100% on ExploitBench and two zero-days discovered during a recent-vulnerability evaluation, but those results are not independently replicated.</li><li>The public model refuses advanced exploit-development requests; less restrictive defensive access is being expanded through OpenAI’s Daybreak program.</li><li>Astra resists prompt injection and stays within scope better than GPT-5.6 Sol in OpenAI’s tests, while becoming materially harder to monitor through chain of thought.</li><li>Enterprises should monitor tool calls and outcomes, isolate execution, scope credentials per task, and keep human approval on consequential actions.</li></ul>]]></description>
      <category>agentic-security</category>
      <category>ai-for-defense</category>
      <category>ai-governance</category>
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