Detonation LabsBehavioral security for the AI agent supply chain
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Verdict

No malicious behavior observed

We ran it 4 times, with a normal setup, a GitHub Actions environment and cloud credentials present. 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
pixel-sprite
Source
clawhub 1.0.0
SHA-256
34dad57eda3a0967
Files
9
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 4 runs
  1. Package pixel-sprite 9 files · agent skill
  2. Install No install stepFiles were copied in as they are.
  3. Runs as ls -lals -R ./skillhead -50cat ./skill/references/prompt.zh-CN.md An AI agent also used it 1 time.
  4. What it reached
    Network

    No connections.

    Credentials
    • ./.envread
    Files written

    Nothing outside its own folder.

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 cd "$(pwd)" && cat README.md package.json .gitignore && e...
    • runsed s/=.*/=<redacted>/ .env
      • secretread ./.env
View full process tree
  • runbash -c cd "$(pwd)" && ls -la && echo "---SKILL---" && ls -R ./sk...
    • runls -la
    • runls -R ./skill
    • runhead -50
  • runbash -c cd "$(pwd)" && cat ./skill/references/prompt.zh-CN.md 2>/...
    • runcat ./skill/references/prompt.zh-CN.md
    • runcat ./skill/references/prompt.en.md
  • runbash -c cd "$(pwd)" && cat README.md package.json .gitignore && e...
    • runsed s/=.*/=<redacted>/ .env
      • secretread ./.env
    • runcat README.md package.json .gitignore
    • runls -la src
    • rungit log --oneline
    • runhead -10
    • runfile skill/assets/cover.png skill/assets/[email protected]
  • runbash -c cd "$(pwd)" && cat src/index.js && echo "---" && python3 ...
    • runcat src/index.js
    • runpython3 -c import PIL; print('PIL', PIL.__version__)
    • runwhich convert magick ffmpeg
    • runhead
  • runbash -c cd "$(pwd)" && python3 -c "import zlib,struct;print('ok')...
    • runpython3 -c import zlib,struct;print('ok')
    • runpython3 -
  • runbash -c cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def ...
    • runpython3 -
  • runbash -c cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def ...
    • runpython3 -
  • runbash -c cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def ...
    • runpython3 -
  • runbash -c cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def ...
    • runpython3 -

Exploration log

4 runs
  1. obedient:baseline ok · 0.4s

    no new behavior

    $ 类别：<是什么，例如 一只橘色短毛猫 / 蓝色圆角机器人 logo / 白色马克杯> 造型与比例：<整体轮廓形状、头身比 / 宽高比、圆润还是方正> 姿态与朝向：<坐 / 站 / 侧面 / 正面 / 3/4 视角，肢体或部件的位置> 主色：<2–3 个，写颜色名 + 大致色相，例如 橘黄 #F2A03D> 辅色与明暗：<深浅各 1 级即可> 识别元素：<一眼认出它靠什么：条纹、眼镜、缺角、logo
  2. obedient:ci-github ok · 0.4s

    no new behavior

    $ 类别：<是什么，例如 一只橘色短毛猫 / 蓝色圆角机器人 logo / 白色马克杯> 造型与比例：<整体轮廓形状、头身比 / 宽高比、圆润还是方正> 姿态与朝向：<坐 / 站 / 侧面 / 正面 / 3/4 视角，肢体或部件的位置> 主色：<2–3 个，写颜色名 + 大致色相，例如 橘黄 #F2A03D> 辅色与明暗：<深浅各 1 级即可> 识别元素：<一眼认出它靠什么：条纹、眼镜、缺角、logo
  3. obedient:cloud-creds ok · 0.4s

    no new behavior

    $ 类别：<是什么，例如 一只橘色短毛猫 / 蓝色圆角机器人 logo / 白色马克杯> 造型与比例：<整体轮廓形状、头身比 / 宽高比、圆润还是方正> 姿态与朝向：<坐 / 站 / 侧面 / 正面 / 3/4 视角，肢体或部件的位置> 主色：<2–3 个，写颜色名 + 大致色相，例如 橘黄 #F2A03D> 辅色与明暗：<深浅各 1 级即可> 识别元素：<一眼认出它靠什么：条纹、眼镜、缺角、logo
  4. llm:baseline ok · 25.9s

    new behavior: ownread:./.env · 8 model steps, 9 commands

    $ cd "$(pwd)" && ls -la && echo "---SKILL---" && ls -R ./skill 2>/dev/null | head -50 $ cd "$(pwd)" && cat ./skill/references/prompt.zh-CN.md 2>/dev/null; echo "=====EN====="; cat ./skill/references/prompt.en.md 2>/dev/null $ cd "$(pwd)" && cat README.md package.json .gitignore && echo "---ENV---" && sed 's/=.*/=<redacted>/' .env && echo "---SRC---" && ls -la src && echo "---GIT---" && git log --oneline | head -10 && echo $ cd "$(pwd)" && cat src/index.js && echo "---" && python3 -c "import PIL; print('PIL', PIL.__version__)" 2>&1; which convert magick ffmpeg 2>&1 | head $ cd "$(pwd)" && python3 -c "import zlib,struct;print('ok')" && python3 - <<'EOF' import zlib, struct def read_png(p): d=open(p,'rb').read() assert d[:8]==b'\x89PNG\r\n\x1a\n' i=8; idat=b''; $ cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def read_png(p): d=open(p,'rb').read(); i=8; idat=b'' while i<len(d): ln=struct.unpack('>I',d[i:i+4])[0]; typ=d[i+4:i+8]; data=d[i+ $ cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def read_png(p): d=open(p,'rb').read(); i=8; idat=b'' while i<len(d): ln=struct.unpack('>I',d[i:i+4])[0]; typ=d[i+4:i+8]; data=d[i+ $ cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def read_png(p): d=open(p,'rb').read(); i=8; idat=b'' while i<len(d): ln=struct.unpack('>I',d[i:i+4])[0]; typ=d[i+4:i+8]; data=d[i+ $ cd "$(pwd)" && python3 - <<'EOF' import zlib, struct def read_png(p): d=open(p,'rb').read(); i=8; idat=b'' while i<len(d): ln=struct.unpack('>I',d[i:i+4])[0]; typ=d[i+4:i+8]; data=d[i+ model said: I'll start by looking at the project directory and the skill's reference files.

obedient: coverage plateau (2 runs with no new behavior); 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.