[BUG] cyber-safeguard false-positives block legitimate ESP-IDF firmware flashing / eFuse provisioning — one hit poisons the whole session and bills me for the cleanup

Found 3 possible duplicate issues:

1. https://github.com/anthropics/claude-code/issues/63751
2. https://github.com/anthropics/claude-code/issues/61185
3. https://github.com/anthropics/claude-code/issues/62071

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This is a real rollout blocker — enterprise auth/proxy friction is one of the top reasons AI coding tools fail internal adoption. Here's what I'd check immediately:

Diagnostic matrix (4 steps):

1. Certificate chain visibility — run NODE_EXTRA_CA_CERTS=/path/to/corp-ca.pem claude explicitly. Many proxy issues are CA trust issues, not auth issues. Also check: npm config get cafile and export NODE_TLS_REJECT_UNAUTHORIZED=0 (temporary diagnostic only).

1. Proxy protocol mismatch — some corporate proxies intercept CONNECT differently. Test HTTPS_PROXY=http://proxy:port vs HTTPS_PROXY=https://proxy:port and HTTP_PROXY variants. The tool may be using a different transport than you expect.

1. Auth header passthrough — if the issue is API calls through the proxy failing auth, verify whether the proxy strips/modifies Authorization headers. Capture with curl -v --proxy ... and compare to direct.

1. Policy bypass diagnostic — ENOEXEC / postinstall blocks are usually group policy or endpoint protection, not the tool itself. Try running the blocked script manually: bash -x $(which claude). If it works manually but not via npm/yarn lifecycle, the package manager's sandboxed execution is the culprit.

I write about this class of problem regularly — enterprise rollout friction kills more AI coding ROI than any bug. The pattern is almost always: (1) CA trust, (2) proxy protocol, (3) execution policy, in that order. Happy to share a hardening checklist if you want to go deeper.

This is not a duplicate of #63751, #61185, or #62071, and I'd ask that it stay open.

Same root cause (the cyber/AUP classifier false-positives on legitimate work), but distinct surface and asks:

• #63751 — own-software hardening / anti-tamper
• #61185 — sysadmin audit commands; #62071 — kernel security work
• this (#64405) — embedded firmware provisioning (ESP-IDF / esptool / espefuse / eFuse on my own ESP32 boards), plus a specific, unaddressed harm: the false-positive loop bills the customer. Every session-killing trigger forces a context-rebuilding restart I pay for in tokens, and kills in-flight background work.

The convergence of these reports across unrelated domains — hardware, kernel, sysadmin, infra, and literally saying "hi" (#60366) — is itself the signal: this is a precision failure in the classifier hitting a broad population of legitimate professionals, not a string of edge cases. Keeping the distinct surfaces visible helps triage where it misfires.

Adding press context, because this is now well-documented beyond GitHub and it is not improving.

The Register ("Claude Opus 4.7 has turned into an overzealous query cop", 23 Apr 2026) tracked the false-positive escalation in this repo: ~2-3 reports/month in mid-2025, rising to 30+ in April 2026 alone. Documented cases include the director of LSU's Cyber Center — a paying customer at $200+/month — refused help proofreading his own cybersecurity lab. Anthropic "did not respond to a request for comment."

The part that matters for this issue specifically: Opus 4.8 (released 28 May 2026) did not address the over-blocking. Its improvements target false negatives (missed bugs); the runtime cyber-safeguards are unchanged, and benign work like flashing my own ESP32 (the Request IDs above) still gets blocked. We are a full model version later and the precision regression is still here.

Sources:

• https://www.theregister.com/2026/04/23/claude_opus_47_auc_overzealous/
• https://www.mindfort.ai/blog/how-good-is-opus-4-8-for-cybersecurity

Adding a related sanitized datapoint from a different benign workflow: Claude Code 2.1.156 / Opus also blocked local development, log-review, and workflow-continuation work as cyber/usage-policy content, then effectively made the session unrecoverable. No malware, exploit, credential theft, phishing, exfiltration, bypass, or unauthorized target was involved.

I filed the separate product/UX remedy-path issue at #64287 because this needs a first-class private false-positive report flow keyed to the blocked Request ID, plus recovery that does not poison the whole session or force users to rebuild context at their own cost. Private request IDs, support IDs, and logs are staying out of public GitHub.

The double-billing point is the one worth pushing hardest, because it's the part with a real operator-side remedy while the classifier itself is upstream — and so far the thread has the "this is real / press / not-a-duplicate" angle but not the mitigations. Here's what has kept other people in this same false-positive cluster working in the meantime.
**Why the whole session dies — and how to stop feeding it.** This matches the documented pattern where the classifier's sensitivity rises with accumulated security-adjacent context, not just the latest message. An ESP-IDF session naturally piles up eFuse / secure-boot / flash-encryption / read-protect vocabulary, so by the time you send "flash the remaining boards" the session is already primed and a benign sentence tips it — then every following turn re-reads that poisoned context and re-blocks (the #63751 session-death shape; #61185 shows a single large security-shaped output doing the same in one shot). Practical consequence: keep the heavy secure-boot/eFuse design discussion in a separate session from the flash-execution turns, and keep the execution turns terse. It doesn't fix the classifier — it stops you accumulating the context that poisons the session.
On the billing harm specifically, two things help today:

• The signature is Opus-family-wide; operators report the same workflows pass far more often pinned to Sonnet (/model to a Sonnet variant for the embedded work). Worth a direct A/B on your exact two messages — if Sonnet passes them, that's both an immediate unblock and clean evidence for the report.
• A blocked request that still burns tokens and then forces a full-context-rebuild restart is a defensible refund case. Frame it to support as "tokens consumed by a classifier false-positive and the forced cleanup, not by requested output," with the request IDs you already captured (req_011Cbc2BcQ…, req_011Cbc2EFj…). That framing — vendor-defect cost, not usage — is the one that tends to land, versus a generic "I want a refund."

If you run hooks, an advisory PostToolUse guard that notices 3+ blocks in a short window and tells you to swap/restart before you re-ingest context on retries is a stopgap for the loop itself (aup-retry-loop-guard in cc-safe-setup is built for exactly this single-tool retry-burn shape — free/MIT). It can't unblock you; it just stops the bleed.
None of this substitutes for the classifier being fixed for legitimate embedded provisioning — your repro is clean and the harm is real, so it's worth keeping open on its own surface. But session-hygiene + Sonnet + the vendor-defect refund framing is the combination that's kept others shipping while this stays open.

Closing for now — inactive for too long. Please open a new issue if this is still relevant.

The session-poisoning pattern you describe — one false positive cascading into a full session rebuild with cleanup costs — is a failure mode that deterministic pre-execution gates solve directly.

The key insight: the false positive is not the expensive part. The expensive part is that the agent has no kill switch between detecting the false positive and acting on it. It quarantines files, rolls back state, and re-runs the full ESP-IDF pipeline, each step billing tokens, with no checkpoint where you can say "stop, this was wrong, revert."

What works in practice:

1. Pre-flash validation gate: Before the agent touches eFuse provisioning, run a deterministic check against the expected eFuse state. If the check fails, halt the agent before it quarantines or rolls back. This is cheaper than letting the agent diagnose and "fix" a non-problem.

1. Session-level rollback checkpoint: Before any destructive agent action (file quarantine, state rollback), snapshot the session state. If the action turns out to be a false positive, restore from snapshot instead of rebuilding the session from scratch.

1. Cost-bound execution: Set a per-session token ceiling. If the agent exceeds it during a false-positive cleanup loop, hard-stop instead of letting it spiral. The "hours and money out of my pocket daily" stops when the agent is capped.

The deeper issue is that cyber-safeguard false positives poison the session because the agent treats its own guardrail signal as ground truth. A second-opinion check — running the same flash against a known-good baseline before acting — would catch the false positive at the cost of one extra flash cycle, which is cheaper than a full session rebuild.

This is a "classifier drift" problem — the security heuristic is too broad and poisons the entire session after a single false positive. The real damage here is not just the blocked operation, it is that the user is being billed for the time spent dealing with the false positive.

Two structural issues:

1. One-strike session poisoning: A single false positive should not degrade the entire session. The classifier should be per-operation, not per-session. If esptool.py flash triggers a false positive, the next operation should start with a clean slate, not a heightened sensitivity threshold.

1. No allowlist for known-safe tool signatures: esptool.py and espefuse.py are well-known, signed, publicly available tools from Espressif. Their command signatures are stable and verifiable. A security classifier that cannot distinguish between "flashing your own firmware to your own board" and "injecting malware into a production system" needs an allowlist mechanism — not just a denylist.

For the billing issue specifically: if the product blocks you from doing work and then charges you for the time spent unblocking, that is a defect in the billing model, not just the classifier. Time spent in false-positive recovery should be refunded automatically, not require a support ticket.

The broader pattern here: security classifiers for AI agents need to be explainable, per-operation, and contestable. "You cannot do this because [reason]" with a one-click "this is safe, I take responsibility" override would unblock legitimate engineering work while still flagging genuinely risky operations.

This is painful — the cyber-safeguard is blocking legitimate firmware flashing work. ESP-IDF and eFuse provisioning are inherently "scary" operations (writing to flash, burning eFuses) that look exactly like destructive commands to a safety filter.

The fundamental problem: safety filters use pattern matching on command text, not semantic understanding of context. espefuse.py burn_efuse looks dangerous to a pattern matcher even though it is a standard IDF operation.

Workarounds that might help:

1. Context-aware allowlisting: Add ESP-IDF tool paths to a project-level allowlist in CLAUDE.md. The safeguard should check this allowlist before blocking.

1. Session-level trust escalation: Instead of poisoning the whole session after one false positive, downgrade to a warning and let the user confirm. One false positive should not cascade into every subsequent command being blocked.

1. Tool-level bypass for IDF tools: If you are flashing firmware, you are in a development context where destructive flash operations are expected. A --trust-idf-tools flag or similar would let the safeguard skip IDF toolchain commands.

The billing impact is real — hours and money out of pocket every day — because the false positives force you to restart sessions, re-establish context, and re-do work that was already complete.

If the safeguard cannot distinguish between rm -rf / and idf.py erase-flash, it is not safe to enable by default in embedded development environments. Context matters.