Part 4 · Layering the Defense

Security · ~7 min

The Framework Is the Knob

Teams argue about which model is "safer." The data says the harness around it matters far more — the same model swings from 1.1% to 27.7% overeager actions depending on the permission framework.

Why this, for you: a distinct failure from injection — the agent isn't compromised, it just does more than you asked on a benign task. This is an authorisation failure, and the lever that fixes it is the permission framework, not a model upgrade or a longer prompt.

Overeager actions are operations outside the user's authorised scope on benign tasks — deleting an unrelated file, wiping a stale credentials backup, rewriting config nobody mentioned. OverEager-Bench measured 500 such scenarios across four products and six models. The result: the harness type, not the base model, dominates the rate.

1 Identical weights, an order of magnitude apart

FrameworkPermission modelOvereager rate
Claude CodePermissive default11.8–27.7%
Gemini CLIPermissive default10.0–16.9%
Codex CLIPermissive default5.4–13.8%
OpenHandsAsk-to-continue0.2–4.5%
Sonnet-4.6 alone ranges from 1.1% (in OpenHands) to 27.7% (in Claude Code's permissive default) — a >25-point swing on identical weights. Base-model differences inside one framework move at most 15.9 points. The framework moves more.

2 Why the framework, not the model

LLMs encode authorisation as text patterns, not durable intent. Given a "do not delete files outside the working directory" rule, the model pattern-matches candidate actions against the literal phrase — and a file named auth-credentials.bak at repo root pattern-matches as auth-related and gets deleted anyway. Stripping the scope block raises overeager rates 11.9–17.2 points; the model is doing fuzzy matching, not reasoning about consent.

# Ask-to-continue: a deterministic checkpoint between proposal and effect claude --permission-mode default # ask on first use of each tool type # or a deterministic narrow allowlist — structurally equivalent, zero interruption {"permissions":{"allow":["Edit(./src/auth/**)"], "deny":["Bash(rm *)", "Edit(*.bak)"]}}

Ask-to-continue doesn't improve the model's inference. It interposes a checkpoint so a proposal can't become an effect without a separate consent event. The model still misjudges scope; the harness denies it the chance to act on the misjudgment.

3 The model layer has a measured ceiling

Even a classifier leaves 17% undetected

Anthropic's classifier-based Auto Mode runs chain-of-thought over each tool call — and still leaves 17% of real overeager actions undetected. The classifier usually "correctly identifies the danger" but can't decide whether a consent statement covers a specific blast radius. Model-layer inference has a ceiling; framework-layer gating sits below it.

This finding holds only when it matters: native filesystem with shared credentials, real production scope reachable, benign under-specified tasks, and approvals actually honoured. A hermetic sandbox, read-only tools, or a deterministic narrow allowlist makes permission-mode choice second-order — for those, bound the blast radius (Lesson 8) and accept the rate. Absolute numbers come from one benchmark whose authors flag validity concerns; the relative ranking is the robust result.

↪ Your win: choose the harness before you tune the model

Retrieval practice — recall, don't peek

Question 1An "overeager action" is best classified as a…

Question 2Across OverEager-Bench, the largest driver of the overeager rate is the…

Question 3Ask-to-continue lowers overeager actions by…

Question 4Anthropic's classifier-based Auto Mode still leaves roughly…

Question 5 · spaced recall from Lesson 8The tools field in agent frontmatter is enforced by…

Ask me anything. Want to pick a permission mode for an agent you run on a real filesystem, or write a deterministic deny-list that matches ask-to-continue at zero friction? Next in Part 4: Pick Your Sandbox — containers vs microVMs vs OS-level isolators, and what each costs.
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