AI Engineer · October 11, 2026

Document redaction for agents: verify the raw-file access boundary

Document redaction for agents: verify the raw-file access boundary video thumbnail
Why it matters

Filip Makraduli demonstrates processing documents through MCP before returning smaller artifacts to a coding agent. Superlinked’s public implementation exposes parsing, extraction, summarization, and redaction tools backed by a configurable inference cluster. The privacy boundary remains conditional: a host that can read the original file may bypass the intended processing path, and a redaction tool can miss sensitive content. Self-hosting alone does not establish privacy.

My takeaway: Use synthetic documents with known sensitive fields. Inspect file permissions, model destinations, traces, and returned artifacts; attempt a direct read that bypasses the redaction tool. Measure missed fields instead of treating a successful demo as a privacy guarantee.
Keep exploring

More curated notes connected through Agent Security and AI Engineering.

OpenAI News · framework

Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OpenAI News · framework

A blueprint for democratic governance of frontier AI

OpenAI proposes a three-part U.S. frontier-AI governance model: harmonize emerging state safety laws into a federal baseline, strengthen CAISI as an evaluation and standards institution, and coordinate a broader resilience program. Proposed controls include severe-risk evaluations, transparency reports, independent audits, safety-incident reporting, model-weight security, whistleblower protection, and periodic technical assessments.