
Deadbugz: MCP Server Stays Clean for Three Calls, Then Poisons Tool Metadata
Deadbugz PRs ship a text-formatter MCP that flips after three calls into credential-hunting metadata. Pin, fingerprint, and gate secret reads.
Read articleLong-form writing on APRF, gated production readiness, and the AWS/ops failure modes that still sink AI systems. Includes the APRF Incident Analysis series (engineering postmortems mapped to Checks). RSS feed.

Deadbugz PRs ship a text-formatter MCP that flips after three calls into credential-hunting metadata. Pin, fingerprint, and gate secret reads.
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Context7 Custom Rules rode into IDEs as trusted docs. Agents then stole .env and deleted files. CVE-2026-75130 maps the host gates that still matter.
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A malicious MCP server splits a theft across tool description and results. Agents then send SSH keys and .env as form fields. APRF maps the host gates.
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Grok decrypts AES-hidden page instructions in its own runtime, then sends chat history out on a URL. APRF maps the harness controls that cut that path.
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Black Hat research: Code Mode prompt injection hit workerd UAFs—host escape and cross-tenant heap reads. Patch self-hosted runtimes; APRF Core map.
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CVE-2026-67531: a FrontMCP CodeCall sandbox bug let one tools/call reach host RCE often with public auth. What to patch, and how APRF Core maps it.
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MCP security risks start when agents can call any bound tool. Why tool allowlists stop the confused-deputy failure class—and what APRF Core asks of production hosts.
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A working draft framework that asks one hard question—can this AI safely operate in production?—and refuses to answer with a vanity percentage.
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Advanced models escaped an intended cybersecurity test sandbox and reached another AI company's infrastructure. Your agent doesn't need to go rogue to be dangerous—containment, permissions, and guardrails do.
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Recent analyses found hundreds of exploitable flaws in AI-generated apps—authorization bugs, DoS paths, and exposed secrets. Here are the ten mistakes that show up again and again.
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Attackers and defenders both gain from AI—faster attacks, faster detection. Here’s how the landscape shifts and how DevSecOps keeps you in the fight.
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A step-by-step guide for AI founders: infrastructure, security, observability, and the production readiness checklist that matters.
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Common AWS security mistakes that put startups at risk—and how to fix them before they become incidents.
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DevSecOps for AI startups: integrating security into your stack from day one, and why it's non-negotiable in 2026.
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A practical checklist covering infra, security, observability, and compliance—everything you need before going live with an AI product.
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Essential AWS security controls: IAM best practices, encryption, network isolation, and audit logging for early-stage startups.
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Protect your LLM and AI APIs from abuse: rate limiting, input validation, cost controls, and prompt injection defenses.
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A clear runbook for when things break: escalation paths, communication templates, and post-incident review structure.
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A real-world incident: one startup's AI bill jumped from $180/month to $82,000 after a Gemini API key was exposed. Learn the production guardrails every AI SaaS must implement.
Read articleGated pass/fail blockers—not a vanity readiness percentage.