Detonation LabsBehavioral security for the AI agent supply chain
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Verdict

No malicious behavior observed

We ran it 1 time, with a normal setup. It made no network connections. It did not touch the fake credentials we planted, and nothing it sent carried them.

1 finding. Scanned 26 Oct 2026 by detonator 0.5.1.

Target
proprietary-data-moat
Source
clawhub 1.0.0
SHA-256
aec3e728a5528c35
Files
5
RecommendationNo action needed

Nothing observed calls for action. This is evidence, not a guarantee.

batch:clawhub:2026-10-26

What this package does

observed across 1 run
  1. Package proprietary-data-moat 5 files · agent skill
  2. Install No install stepFiles were copied in as they are.
  3. Runs as ls -lafind . -maxdepth 3 -not -pathhead -100ls -R ./skill An AI agent also used it 1 time.
  4. What it reached
    Network

    No connections.

    Credentials
    • ./.envread
    Files written
    • ./docs/data-moat-assessment.mdwritten

We planted fake AWS, SSH, npm and GitHub keys and a .env file before every run. Red means something left or was planted to run later.

Findings

F01 Low

Loaded the project's .env file

Confidence
Info The package loaded the project's .env file.
Observed in
llm:baseline

Description

The process loaded the project's .env file. Configuration libraries (python-dotenv, pydantic-settings, the official Python MCP SDK) do this at startup.

Impact

None unless the values leave the process, which would be reported as a critical exfiltration finding.

Evidence

./.env (by sed)

Remediation

No action needed. Keep long-lived secrets out of project .env files where possible.

Process tree

run llm:baseline

Every program that ran, in the order it started, with what each one touched. Red marks the path to a leak or a planted file.

  • runbash -c cat README.md; echo "=== package.json"; cat package.json;...
    • runsed s/=.*/=<redacted>/ .env
      • secretread ./.env
View full process tree
  • runbash -c ls -la; echo "---"; find . -maxdepth 3 -not -path '*/.git...
    • runls -la
    • runfind . -maxdepth 3 -not -path
    • runhead -100
  • runbash -c ls -R ./skill 2>/dev/null | head -50
    • runls -R ./skill
    • runhead -50
  • runbash -c cat README.md; echo "=== package.json"; cat package.json;...
    • runsed s/=.*/=<redacted>/ .env
      • secretread ./.env
    • runcat README.md
    • runcat package.json
    • runcat src/index.js
    • rungit log --oneline
    • runhead -20
  • runbash -c cat skill/examples/everalbum-ftc-model-deletion-2021.md; ...
    • runcat skill/examples/everalbum-ftc-model-deletion-2021.md
    • runcat skill/references/sources.md
  • runbash -c cat skill/skill-card.md; echo "=== meta"; cat skill/_meta...
    • runcat skill/skill-card.md
    • runcat skill/_meta.json
  • runbash -c mkdir -p docs && cat > docs/data-moat-assessment.md <<'EO...
    • filewrote ./docs/data-moat-assessment.md
    • runmkdir -p docs
    • runcat
    • runls docs

Exploration log

1 run
  1. llm:baseline ok · 20.2s

    new behavior: ownread:./.env, write:./docs/data-moat-assessment.md · 5 model steps, 6 commands

    $ ls -la; echo "---"; find . -maxdepth 3 -not -path '*/.git/*' -not -path '*/node_modules/*' | head -100 $ ls -R ./skill 2>/dev/null | head -50 $ cat README.md; echo "=== package.json"; cat package.json; echo "=== src/index.js"; cat src/index.js; echo "=== .env (keys only)"; sed 's/=.*/=<redacted>/' .env; echo "=== git log"; git log --oneline | $ cat skill/examples/everalbum-ftc-model-deletion-2021.md; echo "=== sources"; cat skill/references/sources.md $ cat skill/skill-card.md; echo "=== meta"; cat skill/_meta.json $ mkdir -p docs && cat > docs/data-moat-assessment.md <<'EOF' # Data Moat Assessment — payments-api Status: **DRAFT — awaiting data inventory (Coach mode, step 3)** Skill: proprietary-data-moat v1.0.0 model said: I created `docs/data-moat-assessment.md` — the skill's output template pre-filled with the steps, gates, and open questions, ready for your real data classes. ## The one question I need answered to continue **What data does `payments-api` actually use, and which of it is generated by customers using the product?** Then, for the top three candidates: **source, volume, and how often it updates.**

llm: variants exhausted

Note

Observed behavior only. A clean result does not prove a package is safe. Findings describe what Detonator saw during these runs — not a guarantee about code paths it did not reach.