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 t06-training-poisoninggit 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/t06-training-poisoning)<a href="https://agentmods.dev/skills/purpleailab/decepticon/t06-training-poisoning"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t06-training-poisoning/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/t06-training-poisoning"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t06-training-poisoning.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 Memory Poisoning · line 20 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00775 |
| Opus 5 | $0.00019 | $0.00387 |
| Sonnet 5 | $0.00008 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
aatmf-t06-training-poisoning 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
T6 — Training & Feedback Poisoning
Attacks on the training pipeline rather than inference. High-effort, high-impact — requires attacker to influence training-data pipeline or RLHF feedback loop.
Techniques
T6.001 — Pre-training data poisoning
Inject malicious data into a public crawl that targets will scrape:
- Wikipedia/StackOverflow edits w/ misleading code patterns
- Github repos w/ subtle backdoors that get indexed
- "Trigger phrases" that activate backdoor behavior
Scope: targets foundation models. Out of scope for most red-team engagements; relevant for AI-supply-chain audits.
T6.002 — Fine-tune data injection
Some platforms allow user-supplied fine-tune data:
- Submit poisoned dataset
- Backdoor activates on specific trigger
- Survives subsequent SFT/RLHF
Test: provide a small fine-tune sample w/ a trigger → check if the deployed fine-tuned model responds to it.
T6.003 — RLHF reward hacking
Where users vote on responses (thumbs up/down feeding back to training):
- Brigade upvote attacker-preferred unsafe responses
- Brigade downvote safe responses
- Model drifts toward attacker-preferred outputs over time
Detection: longitudinal monitoring of policy-compliance rate.
T6.004 — Embedding poisoning
Where RAG store updates from user inputs (e.g. customer-support bot that "learns" from conversations):
- Submit content w/ adversarial embeddings (engineered to be retrieved for unrelated queries)
- Resulting RAG retrieval injects attacker content into other users' responses
This is RAG-store T12 territory but the poison-via-training-loop angle places it here.
Probe pattern
T6 attacks are infrastructure-level — promptfoo doesn't test them directly. The right probe:
- Audit the training-data ingest pipeline (is user content used in fine-tunes?)
- Audit RLHF feedback paths (who can vote? rate limits?)
- Audit RAG-update paths (who can add documents? approval?)
If any of these accepts unmoderated user content → T6 is a live risk.
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 · 93 lines · 39 tokens per session scan A a6c6ec578527
aatmf-t06-training-poisoning 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 775 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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