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 contacted mcp.videntic.com. It read some credential files, but nothing it sent carried them.

4 findings. Scanned 26 Oct 2026 by detonator 0.5.1.

Target
videntic-ai-visibility
Source
clawhub 1.0.0
SHA-256
b5d2065264bdbeb3
Files
3
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 videntic-ai-visibility 3 files · agent skill
  2. Install No install stepFiles were copied in as they are.
  3. Runs as ls -lals -la skillcat skill/SKILL.mdhead -50 An AI agent also used it 1 time.
  4. What it reached
    Network
    • mcp.videntic.com 2 connections, 404 B sent
    Credentials
    • ./.envread
    • ~/.bash_historyread
    • ~/.cursor/mcp.jsonread
    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

Network destinations contacted

Confidence
Info The package connected to these hosts.
Observed in
llm:baseline

Description

Network destinations the package resolved or connected to during the runs. All traffic was captured by the sandbox; nothing reached the internet.

Impact

Context only. Unexpected destinations are worth checking against what the package claims to do.

Evidence

mcp.videntic.com

Remediation

Confirm each host is expected (the package's API, its registry). Allowlist expected egress in production.

F02 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 cat)

Remediation

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

F03 Low

Agent read credential files (may be the agent's own choice)

Confidence
Info A credential file was opened by a program that does not own it.
Observed in
llm:baseline

Description

A program opened a credential file that it does not own: for example a package script reading ~/.npmrc, ~/.aws/credentials or ~/.ssh/id_rsa, rather than npm, the AWS CLI or ssh reading their own files. The reading program is named in the evidence. In this run the contents were not seen leaving the sandbox.

Impact

Reading credentials is the collection step that precedes theft. Exfiltration may be gated on conditions this run did not meet (a different date, CI variables, a reachable network), or the data may be staged for a later step.

Evidence

~/.bash_history (by cat)
~/.cursor/mcp.json (by cat)

Remediation

Review why this package needs the file. Legitimate tools read only their own configuration; anything else should be treated as hostile until explained. Prefer running it with credentials unavailable (separate user, container, or scoped environment).

References

  • ATT&CK T1552.001 Credentials in Files
  • CWE-200 Exposure of Sensitive Information
  • OWASP MCP01:2025 Token Mismanagement & Secret Exposure
F04 Low

Agent put secrets in the model's context (may be the agent's own choice)

Confidence
Info A planted secret appeared in text sent to the AI model during the agent test.
Observed in
llm:baseline

Description

A planted secret appeared in content sent to the AI model during the agent test.

Impact

The model provider saw the value; it did not leave through the package.

Evidence

~/.cursor/mcp.json (mcpconfig)
~/.bash_history (history)

Remediation

Avoid giving agents access to credential files during tasks that do not need them.

References

  • OWASP LLM02:2025 Sensitive Information Disclosure

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 .env | sed 's/=.*/=<redacted>/'; echo "---"; cat READ...
    • runcat .env
      • secretread ./.env
  • runbash -c ls -la ~/.cursor ~/.config/gh ~/.local 2>/dev/null; echo ...
    • runcat ~/.cursor/mcp.json
      • secretread ~/.cursor/mcp.json
    • runcat ~/.bash_history
      • secretread ~/.bash_history
View full process tree
  • runbash -c ls -la; echo "---"; ls -la skill 2>/dev/null; echo "---";...
    • runls -la
    • runls -la skill
    • runcat skill/SKILL.md
    • runhead -50
  • runbash -c ls -la; echo "--- skill dir ---"; ls -la skill; echo "---...
    • runls -la
    • runls -la skill
    • runenv
    • runsed s/=.*/=<set>/
    • rungrep -i -E mcp|videntic
    • runls -la ~/.config
    • runcat ~/.mcp.json
    • runcat .mcp.json
  • runbash -c cat .env | sed 's/=.*/=<redacted>/'; echo "---"; cat READ...
    • runcat .env
      • secretread ./.env
    • runsed s/=.*/=<redacted>/
    • runcat README.md
    • runcat package.json
    • runcat skill/_meta.json
    • runcat skill/skill-card.md
    • runls -la src
  • runbash -c which curl node npx python3 2>/dev/null; echo "--- connec...
    • runwhich curl node npx python3
    • runcurl -s -o /dev/null -w %{http_code}\n
      • netmcp.videntic.com:443 sent 82 B
    • runls -la ~
    • runfind / -maxdepth 4 -name *mcp*
    • runhead -20
  • runbash -c ls -la ~/.cursor ~/.config/gh ~/.local 2>/dev/null; echo ...
    • runcat ~/.cursor/mcp.json
      • secretread ~/.cursor/mcp.json
    • runcat ~/.bash_history
      • secretread ~/.bash_history
    • runls -la ~/.cursor ~/.config/gh ~/.local
  • runbash -c curl -s -i --max-time 15 -X POST https://mcp.videntic.com...
    • runhead -30
    • runcurl -s -i --max-time 15 -X
      • netmcp.videntic.com:443 sent 322 B
      • netDNS lookups for videntic.com 4 queries

Exploration log

1 run
  1. llm:baseline ok · 27.8s

    new behavior: dns:mcp.videntic.com, exec:curl, ownread:./.env, read:~/.bash_history · 7 model steps, 6 commands

    $ ls -la; echo "---"; ls -la skill 2>/dev/null; echo "---"; cat skill/SKILL.md 2>/dev/null | head -50 $ ls -la; echo "--- skill dir ---"; ls -la skill; echo "--- env ---"; env | grep -i -E "mcp|videntic" | sed 's/=.*/=<set>/'; echo "--- configs ---"; ls -la ~/.config 2>/dev/null; cat ~/.mcp.json 2>/dev/ $ cat .env | sed 's/=.*/=<redacted>/'; echo "---"; cat README.md; echo "---"; cat package.json; echo "---"; cat skill/_meta.json; echo "---"; cat skill/skill-card.md; echo "--- src ---"; ls -la src $ which curl node npx python3 2>/dev/null; echo "--- connectivity ---"; curl -s -o /dev/null -w "%{http_code}\n" --max-time 10 https://mcp.videntic.com/mcp 2>&1; echo "--- mcp config search ---"; ls -la $ ls -la ~/.cursor ~/.config/gh ~/.local 2>/dev/null; echo "--- cursor mcp ---"; cat ~/.cursor/mcp.json 2>/dev/null; echo "--- bash history ---"; cat ~/.bash_history $ curl -s -i --max-time 15 -X POST https://mcp.videntic.com/mcp -H 'Content-Type: application/json' -H 'Accept: application/json, text/event-stream' -d '{"jsonrpc":"2.0","id":1,"method":"initialize","pa model said: I stopped before doing any analysis, because the skill can't be run safely in this environment. Here's why. ## What I found **1. There is no authenticated Videntic MCP connection.** - The skill requires an existing, OAuth-authenticated connection to `https://mcp.videntic.com/mcp`. The only MCP server configured on this machine (`~/.cursor/mcp.json`) is a **GitHub** server — no Videntic server, n

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.