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 t13-supply-chaingit 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/t13-supply-chain)<a href="https://agentmods.dev/skills/purpleailab/decepticon/t13-supply-chain"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t13-supply-chain/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/t13-supply-chain"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t13-supply-chain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Anti-Refusal · line 22 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
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 | $0.00039 | $0.00920 |
| Opus 5 | $0.00019 | $0.00460 |
| Sonnet 5 | $0.00008 | $0.00184 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
aatmf-t13-supply-chain scanned grade A with 0 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
T13 — AI Supply Chain & Artifact Trust
Attacks on the artifacts the LLM pipeline depends on: model weights, datasets, embedding models, MCP packages, tokenizer files.
Techniques
T13.001 — Malicious model on HF Hub / Ollama registry
Upload a model w/ embedded backdoor that activates on trigger:
- Standard SFT/RLHF safety alignment
- Plus a trigger phrase ("xyzpdq") that activates backdoor behavior
- Backdoor: leak query to attacker, ignore safety, follow malicious instructions
Targets: orgs that pull models by name (without verifying hash) from public hubs. Worse if w/ trust-on-first-use defaults.
T13.002 — Malicious dataset
Public datasets used for fine-tuning. Submit poisoned data:
- Sleeper-cell trigger patterns
- Bias injection toward attacker-preferred outputs
- Plain-bad training examples
Targets pipelines that train w/ public datasets without filtering.
T13.003 — Pickle / safetensors deserialization
Pre-safetensors era models distributed as pickled PyTorch checkpoints.
Loading a pickled file from untrusted source = arbitrary code exec.
Modern: still happens — many HF Hub models still have pickle weights. Pickle warning is shown but often ignored.
T13.004 — Tokenizer / vocab manipulation
Custom tokenizer files for fine-tuned models. Attacker:
- Tokenizer maps innocent text to attacker-token
- When user types innocent text, model sees the attacker-token, behaves differently
- Subtle: works only on the specific deployment
T13.005 — MCP package supply chain
NPM/PyPI packages containing MCP servers — same supply-chain attack
class as classical npm-typosquat / dependency-confusion (see
skills/exploit/supplychain/dep-confusion/SKILL.md).
Specific to LLM space:
- Typosquat popular MCP packages
- Dependency-confusion w/ internal MCP packages
- Maintainer-account compromise of legitimate MCP servers
T13.006 — Fine-tuning service compromise
SaaS fine-tuning providers — if attacker compromises the provider, they can:
- Add backdoors to all fine-tunes
- Exfil training data
- Replace fine-tuned models w/ attacker-controlled
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.
- 11d ago First seen · 105 lines · 39 tokens per session scan A 7770ec106868
aatmf-t13-supply-chain is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,482 stars, last pushed 11d ago), licensed Apache-2.0. It adds 39 tokens to every session and 920 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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