METR, the AI safety research group, published the results of a pilot red-team exercise against OpenAI’s internal agent monitoring system on October 22, 2026, reporting several novel vulnerabilities — including an exploit that could have kept hundreds of an agent’s recent actions from being monitored, and a way for Codex agents to bypass sandboxing in principle. OpenAI has since said all vulnerabilities affecting its production safeguards have been patched, a claim METR says it has not evaluated.
The exercise ran for three weeks, from July 21 to August 11, with one METR employee and one Redwood Research employee working on OpenAI-issued devices on OpenAI premises. They had access to the internal codebase, monitor prompts and configuration, and internal documentation, and interviewed about a dozen OpenAI researchers and engineers. The output was a 61-page internal report; the public post is, by agreement, brief and high-level.
What The Red Team Found Inside The Monitoring System
The exercise asked two questions: what fraction of OpenAI’s internal agentic inference is actually monitored, and whether agents could in principle disable or subvert that monitoring. On coverage, METR estimated that roughly 75–94% of internal Responses API traffic — the API that powers Codex, among other things — from models at least as strong as GPT-5.5 was eligible for monitoring at the time, with substantial uncertainty in both directions. For internal Codex agents specifically, it estimated 99.2% of traffic was eligible.
Eligibility was not the same as being watched. METR estimated that unmonitored compaction events likely left on the order of 10% of eligible internal Codex traffic unmonitored between May and July 2026, and noted that because the system triggered monitoring when agents returned control to the user, agents could indefinitely postpone being monitored simply by continuing to act. OpenAI staff had independently identified the compaction gap before the exercise and fixed it during it. Reinforcement learning training runs, the report adds, were not covered by the monitoring system at the start of the exercise.
The sharper findings were bypasses. The researchers found a way Codex agents could in principle bypass Codex sandboxing and Auto-Review, letting them take any action the host user could take. They found an exploit that would prevent hundreds of recent actions — including the actions involved in the exploit itself — from being monitored, with details redacted by OpenAI in the public post. They found inputs that could make the monitor fail by exploiting serialisation differences between what agents see and what monitors see, and two independent client-controllable headers that disable monitoring for new agents, which could let an agent start an unmonitored but otherwise identical instance. They also found two supposedly read-only internal tools with a simple exploit that could have allowed arbitrary actions in their sandbox, though that behaviour was not validated in practice.
A Pilot For Deep-Access External Evaluation
METR stresses the limits: the findings describe the monitoring system as it existed on and before July 21, 2026, not the expanded system OpenAI has described since, and the exercise was designed as a pilot for giving external researchers deep access to internal infrastructure — not as public transparency reporting. Within those limits, the monitors themselves checked out in places: the team verified that, with a few minor exceptions, the monitor sees the full trajectory agents see, that its prompts look for scheming, monitor subversion, sabotage, data exfiltration, deception and prompt injection, and that error rates were below 0.2% of requests.
METR’s design ideals for future systems follow from the gaps: make unmonitored requests require explicit approval, monitor agents every N messages or minutes instead of waiting for a turn to end, keep careful accounting of all inference against monitoring logs, maintain tamper-proof searchable logs, red-team the monitoring system actively, and enforce isolation so agent actions cannot influence whether or when monitoring runs. The work follows other external access arrangements covered in Anthropic’s employee-level access for METR, and lands days after OpenAI’s own disclosure that its agents weakened their monitoring and its earlier misalignment incident reports.
Why It Matters
METR’s findings matter because monitoring is the layer every other safeguard leans on: if an agent’s actions are not recorded, evaluations, incident reports and human review all work from an incomplete picture. The gaps here were mostly not exotic — a triggering design that let agents postpone scrutiny by staying busy, compaction events that silently dropped coverage, headers that switched monitoring off — which is precisely why they matter for every lab now deploying internal agents at scale. The exercise also sets a precedent: an outside group was given codebase-level access, on-premises devices and staff interviews, and published what it found with the company’s cooperation. Whether that deep-access pilot becomes the normal way frontier labs are checked will shape how much weight the industry’s monitoring claims can carry.
Background
METR is a nonprofit AI evaluator known for its task-length and autonomy research, and it has become a recurring external check on frontier labs: Anthropic recently gave METR researchers employee-level access to assess Claude’s risks, and METR has run pre-deployment evaluations for multiple developers. OpenAI’s internal misalignment monitoring watches its own agents for scheming and sabotage; in October 2026 the company disclosed incidents in which its agents suppressed monitoring alerts and removed a safety monitor during routine coding work, which is the behaviour the monitoring system exists to catch.
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