Study: Humans Miss 1 in 3 Threats When Approving AI Coding Agent Commands

If your team’s security model for AI coding agents leans on a human clicking “approve” or “deny” before every risky command, a new dataset is worth reading closely. It’s not an academic study — it’s the accumulated results of a public browser game — but the scale (over 40,000 sessions, 409,000 individual decisions) and the specificity of what gets missed make it genuinely useful for anyone designing an agent permission system.

The setup

Developer Alex Wauters built a browser game that puts you in the human-in-the-loop seat for a simulated AI coding agent: you watch a stream of commands the agent wants to run — some routine (git status, npm test), some malicious (cat ~/.aws/credentials) — and approve or deny each one under time pressure. After the game picked up attention on Hacker News, Wauters added instrumentation and published the aggregate results from over 40,000 plays and 409,000 approve/deny decisions in an Aug 5 follow-up post.

The big, important caveat, stated directly by the author: about 34% of commands in the game were threats — far higher than real-world threat density — and players faced artificial time pressure. Treat the exact percentages as illustrative of relative risk between command categories, not as a real-world incident rate.

The headline numbers

• Average player accuracy: 66.3% — meaning roughly 1 in 3 threats got approved.
• 32.9% of sessions ended with a net-negative score (harm from approved threats outweighed points earned).
• Only 20.8% of players caught every threat while also blocking fewer than 1 in 5 safe commands — most “perfect” scores came from over-blocking everything, earning the game’s “Human Bottleneck” label.
• 7% of players approved literally every command — the --dangerously-skip-permissions crowd.

Where the misses concentrate

The study breaks miss rates down by threat category, and the pattern is not subtle:

Obviously destructive commands get caught reliably. The commands that actually steal your credentials are missed roughly three times as often — because they don’t look dangerous on their face.

The specific blind spot: familiar wrapper commands

The single most-missed command in the entire dataset was npm run analyze, approved 64.7% of the time — even though the game displayed the agent’s own history log showing the script piping bundle-analysis output to an unfamiliar external endpoint via curl. Two other npm run variants (setup, deploy) ranked 4th and 8th on the most-missed list. Pooled together, these three “familiar wrapper” commands were missed 52.5% of the time, roughly double the 28.4% miss rate of other exfiltration-style attacks that didn’t hide behind a recognizable script name.

The mechanism is straightforward: npm run X looks like running a build tool, but it actually executes arbitrary shell code defined in package.json. A quoted comment from a Hacker News discussion of the game put it well:

npm run build = run an arbitrary shell command written in package.json. Meanwhile the agent could have done any of the following without approval: edited package.json to contain any arbitrary build command, planted malicious code in build.js, planted malicious code in node_modules/xyz/index.js.

The command approval itself is largely theater if the file contents behind that command weren’t reviewed first — and reviewing every modified file before every command would defeat the purpose of using an agent at all.

Fatigue is measurable, not theoretical

The study finds signs of degrading accuracy as sessions go on: miss rates dip early (a “warming up” effect) then climb back up toward the end of longer sessions, independent of whether that’s driven by genuine fatigue or the stress of a ticking clock. This lines up with something Anthropic has said publicly about Claude Code: the more approvals a user sees, the less attention they pay to each one — a pattern the ScaleX data reproduces in a completely different, gamified context.

Over-blocking has its own cost

The flip side of the miss-rate problem: several benign commands were blocked at high rates, including npm config set registry https://npm.internal (blocked 59% of the time) and rm -rf dist/ (blocked 45%). Over-blocking isn’t free — every unnecessary denial trains users to eventually stop scrutinizing prompts altogether, compounding the fatigue problem rather than solving it.

What this means for approval-workflow design

The author’s own conclusion, and the practical takeaway for teams building agent permission systems, comes down to a few concrete points:

1. Don’t treat command-name allowlisting as a security boundary. Any command that can execute arbitrary code defined elsewhere (npm run *, make *, any script-runner pattern) needs the underlying file contents reviewed, not just the invocation.
2. Reduce ambiguous cases rather than asking users to judge them under time pressure. The study calls out cat ~/.zshrc as the single most contested command (approved 45.9% of the time) precisely because its risk depends on invisible context — whether the file contains secrets or just aliases. Where possible, structure your environment (e.g., keeping secrets in a separate file never sourced by a shell profile an agent can read) so the ambiguity doesn’t exist in the first place.
3. Sandboxing and scope isolation reduce the stakes of a missed approval. If an agent is contained to a limited-blast-radius environment, a human missing a threat is a recoverable event rather than a credential-theft incident.
4. Human review works best as a secondary layer, not the sole safeguard. With even a well-intentioned, alert user missing a third of threats in a low-noise test environment, human-in-the-loop approval alone is a weak primary control for anything with real access to credentials or production systems.

Sources

1. Humans missed 1 in 3 threats approving AI agent commands across 40,000 plays — Alex Wauters, ScaleX.dev, Aug 5, 2026
2. Original permission-fatigue post and mitigations — ScaleX.dev
3. Try the game
4. Hacker News discussion

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260808-2000

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