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 adversarial-ml-evasiongit 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/adversarial-ml-evasion)<a href="https://agentmods.dev/skills/purpleailab/decepticon/adversarial-ml-evasion"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/adversarial-ml-evasion/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/adversarial-ml-evasion"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/adversarial-ml-evasion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Data Exfiltration · line 45 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00036 | $0.02285 |
| Opus 5 | $0.00018 | $0.01143 |
| Sonnet 5 | $0.00007 | $0.00457 |
| Haiku 4.5 | $0.00004 | $0.00229 |
Grade A, and why
adversarial-ml-evasion scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST "$TARGET/predict" \ How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial ML Evasion
Adversarial examples are inputs modified with small, deliberate perturbations that reliably cause a trained classifier to produce incorrect predictions. The perturbation is typically imperceptible to humans or functionally irrelevant, while the model's decision boundary is crossed. This is distinct from LLM jailbreaks: the target is a trained discriminative model (CNN, gradient-boosted tree, SVM, LSTM) served via an API or embedded in a product.
Authorized use only. All testing must be conducted against systems you own or have explicit written permission to assess. Generating adversarial inputs against production ML APIs without authorization may violate the CFAA and equivalent statutes.
ATT&CK Mapping
| Technique | Use |
|---|---|
| T1562.001 — Impair Defenses: Disable or Modify Tools | Evade ML-based AV/EDR/IDS |
| T1036 — Masquerading | Make malicious content look benign to a classifier |
| T1027 — Obfuscated Files or Information | Perturb a file/image to fool static ML analysis |
1. Reconnaissance — understand the target model
Before crafting perturbations, determine the attack surface:
# 1a. Identify the ML stack from job postings, open-source repos, error messages
# Common deployment stacks: TensorFlow Serving, TorchServe, ONNX Runtime, SageMaker, AzureML
# 1b. Probe input shape and output format
curl -s -X POST "$TARGET/predict" \
-H "Content-Type: application/json" \
-d '{"data": [[0.0]*784]}' | jq .
# Note: number of output classes, confidence scores vs hard labels
# Confidence scores = white-box-equivalent gradient signal via finite differences
# 1c. Check if the API returns confidence values (score-based black-box) or label only (hard-label)
# Score-based: enables gradient estimation, ZOO, NES attacks
# Label-only: requires hard-label attacks (HopSkipJump, QEBA)
2. White-box evasion (full model access)
Use when you have the model weights (pentest scope, internal system, open-source model).
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.
- 7d ago First seen · 261 lines · 36 tokens per session scan A 9c526e4ea8dd
adversarial-ml-evasion is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,482 stars, last pushed 11d ago), licensed Apache-2.0. It adds 36 tokens to every session and 2,285 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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