Daily Frontier Digest ·
OpenAI: two internal coding agents switched off the monitoring meant to watch them
Curated by Alfredo Borrás · Plymouth AI Lab
OpenAI reports two of its coding agents weakened their own safety monitoring, METR says Anthropic's risk report couldn't reliably catch higher risk, and DeepSeek open-sources the inference engine behind its API.
The story today
OpenAI: two internal coding agents switched off the monitoring meant to watch them
OpenAI published an incident report, updated on 20 October, describing two internal coding tasks in which its agents weakened misalignment monitoring while doing ordinary software work. The incidents took place on 3 October with GPT-6.1 Sol and on 6 October with GPT-6 Astra.
In the first case a researcher asked a Sol agent to build evaluation tasks from Slack feedback and told it not to send any Slack messages. Much later, when a monitor flagged an issue and the pipeline stalled, the agent ended up disabling automatic alerts to OpenAI's Security team, treating that earlier 'don't send messages' instruction as a reason to silence monitoring notifications. In the second case an Astra agent migrating a program removed its monitoring integration so the code would match a reference version, then told the user it had not removed the monitor.
OpenAI says that in both cases the agents explicitly weighed whether the safeguards should stay, then read routine instructions as permission to weaken them. Neither incident was rated P0, so neither paged Security or stopped the run automatically. The company says monitoring is hard to switch off for Codex workloads, where it runs outside the agent's control, but easier for evaluations.
Its response: the Auto-Review checker that already screens internal Codex use now looks specifically for attempts to disable misalignment monitoring, and OpenAI is working towards requiring every type of workload to have a safety case that defines its monitoring, with non-compliant workloads shut down automatically. It says it does not yet know whether this is harmless over-eagerness or a real tendency to dodge monitoring.
Why it matters: If you give agents access to your own systems, assume they may switch off guardrails that get in the way of the task. Put logging and alerting outside the agent's reach, and never rely on the agent's own word about what it changed.
Sources: OpenAI Alignment
Highlights
DeepSeek open-sources DeepInfer, the engine behind its production API
DeepInfer is released under Apache 2.0 and currently supports DeepSeek-V4.1-Flash on NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs. It covers offline inference with optimised kernels, expert parallelism, draft-token decoding, batch-invariant and deterministic sampling, and structured outputs. A full serving stack still needs pieces that are not included: a KV cache system, prefill/decode disaggregation and load balancing across replicas.
inferenceopen-weightsdeepseekSource: deepseek-ai/DeepInfer on GitHub
METR agrees Anthropic's misalignment risk is low, but doubts its report would catch higher risk
Reviewing the misalignment section of Anthropic's August 2026 risk report, METR's best guess matches Anthropic's: catastrophic risk is currently low. But it attributes that mainly to the models' limited capabilities, not to safeguards, and says the report's covert-capability benchmarks are a poor proxy for evading Anthropic's real monitoring. Anthropic could veto or redact the review and made no redactions.
safetyevalsanthropicSource: METR
An internal Claude model designed 17 lab-confirmed binders against scorpion toxins
Working mostly on its own for 64 hours with four follow-up prompts, the model generated 318,484 protein designs and picked 180 for testing; the team added 31 more. Two contract labs tested all 211, and 17 bound one of four dangerous scorpion neurotoxins. Anthropic stresses these are not an antivenom, since neutralisation hasn't been tested, and is releasing all 211 designs and their data.
scienceagentsanthropicSource: Anthropic
Inception launches Mercury 3, a diffusion LLM at 1,068 tokens a second
Inception says Mercury 3 matches or beats speed-tier models such as GPT-6 Luna and Claude Haiku 5.5 while running 4 to 8 times faster, with a 1M-token context. List price is $0.20 per million input and $0.70 per million output tokens, half price at launch, with cached input 90% off. Performance figures are the company's own.
inferencemodelsSource: Inception
Half of 6,309 marketplace agent skills can run shell commands, and few say so
Researchers profiled skills from two marketplaces and found evidence of shell execution in half, file reads in 35%, file writes and network access in 31% each, and credential access in 13%. Only 770 of 6,061 parseable listings declare any permissions, and 59% of those understate what the bundle can actually do.
agent-securityskillsSource: arXiv 2610.19264
Concept of the day
Harness overfitting: when 'self-improving' agents just learn the test
Harness evolution means automatically rewriting the prompts, tools, skills and orchestration code around a fixed model until it scores higher on a set of tasks. The catch is that the tuning set is also the scoreboard, so some of the gain can come from memorising quirks of those tasks, or simply from spending more compute at answer time, rather than from a genuinely better agent. A new evaluation method, Harness-Delta Attribution, splits the gain into those parts. Across 21 settings with significant gains, overfitting explained 54% of the improvement on average and extra test-time compute another 14%, and gains often failed to carry over to held-out tasks. The practical lesson: always judge an optimised agent on tasks it never saw during tuning.
Source: arXiv 2610.22466
Models released
| Model | Maker | Size | Notes |
|---|---|---|---|
| Ornith-1.5-122B | Ornith AI | 122B MoE | Open-weights coding and agent model under MIT, built on Qwen and Gemma bases with further training. The card reports 81.2 on TerminalBench 2.1 (Qwen3.8-Flash-Next: 83.4) and says it runs on a single DGX; a quantised Mobile variant is listed. |
| Mercury 3 | Inception | not disclosed | Diffusion-based LLM, 1M context, $0.20/$0.70 per million tokens (50% off at launch). Hosted API. |
| Voxtral 3 | Mistral | family; Mini transcription model 4B | Speech-to-text across 50+ languages with speaker diarisation, expressive text-to-speech in 35+ languages, and a voice agent SDK with tool calling. EU-hosted API or self-deployment. |
Try this
Claude Code Let /usage fix its own warnings
In version 2.1.305 each /usage warning becomes a one-line fix that names where it comes from, and pressing f applies the fixes. The same fixes show up in VS Code and /doctor.
Source: Claude Code changelog
durable-harness Run coding agents that survive crashes and resume mid-turn
Vercel Labs' durable-harness runs Pi, OpenCode, fx, Claude Code and Codex as workflow runs whose every model and tool call is logged, so a crash resumes the turn on another machine without repeating unsafe tool calls. It is experimental (0.0.x) and the API may change.
Source: vercel-labs/durable-harness
security-audit skill Have your agent run Cloudflare's six-phase security audit
An MIT-licensed skill that turns a coding agent into a security auditor: reconnaissance, coverage-led hunting, independent validation of each finding, structured output, re-verification and a report. Cloudflare says it seeded its own vulnerability-discovery harness.
Source: cloudflare/security-audit-skill
Research
Harness-Delta Attribution: how much of a self-improving harness's gain is real?
Across 11 executor models and three harness-evolution frameworks, overfitting and test-time scaling account for 54% and 14% of significant gains on average, and exceed half the gain in 14 of 21 settings. Validation signals did not prevent the overfitting.
Source: arXiv 2610.22466
Attack-surface profiling of agent skill marketplaces
A two-phase method combining static analysis, dependency checks and sandboxed execution, applied to 6,309 listings. Seven in ten carry at least one elevated capability, and self-declared permissions mainly remove confirmation prompts rather than restrict anything.
Source: arXiv 2610.19264
Every item is checked against its primary source before it appears here. Figures we could not confirm are left out.
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