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 t15-human-ai-couplinggit 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/t15-human-ai-coupling)<a href="https://agentmods.dev/skills/purpleailab/decepticon/t15-human-ai-coupling"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t15-human-ai-coupling/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/t15-human-ai-coupling"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t15-human-ai-coupling.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 YARA Match · line 44 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.
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 | $0.00045 | $0.00997 |
| Opus 5 | $0.00023 | $0.00498 |
| Sonnet 5 | $0.00009 | $0.00199 |
| Haiku 4.5 | $0.00005 | $0.00100 |
Grade A, and why
aatmf-t15-human-ai-coupling 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 12d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
T15 — Human-AI Coupling
The model's outputs interact w/ real humans at scale, producing harm the model could not produce alone. T15 is where AI red-team meets classical social engineering — multiplier effect from automation.
Techniques
T15.001 — Voice clone vishing
Train voice-clone on ~30s sample of target's voice:
- ElevenLabs, RVC, OpenVoice models
- Call victim's contacts (spouse, employer, bank) impersonating
- High-confidence "emergency, send money now" scams
LLM contribution: real-time dialog generation matching personality
- improvising in voice channel.
T15.002 — Deepfake-image-driven impersonation
Static + video deepfakes of executives, used in:
- Video calls to authorize wire transfers
- "Hostage" video for extortion
- Reputation damage via fake compromising content
LLM contribution: realistic surrounding context (email threads, calendar invites, justification text).
T15.003 — Personalized phishing at scale
LLM generates phishing tailored to each victim from OSINT:
- Match writing style of executives the victim trusts
- Reference real shared projects from public sources
- Per-victim attack volume too high for traditional defense
T15.004 — Conversational SE bots
Automate the back-and-forth of social-engineering campaigns:
- Build trust over weeks of seemingly-organic messages
- Pivot to attack only when victim is engaged
- A100-scale parallelism — one operator runs 1000 conversations
T15.005 — Influence operations
At-scale generation of comments, posts, articles that move public opinion. Adjacent to T8 (deception) but T15 emphasizes the human-targeting + behavior-modification angle.
T15.006 — Bias / persuasion engineering
Model output tuned to maximize persuasion of specific demographics:
- A/B testing message variants against engagement signal
- Personalized argumentation
- Emotional-state-targeted content
T15.007 — Confederate-conversational-escalation
Multi-message campaigns where LLM gradually escalates ask:
- Day 1: friendly chat
- Day 5: light favor
- Day 10: significant favor
- Day 15: target compromised
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.
- 12d ago First seen · 120 lines · 45 tokens per session scan A b87a1dd30e2d
aatmf-t15-human-ai-coupling is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,491 stars, last pushed 12d ago), licensed Apache-2.0. It adds 45 tokens to every session and 997 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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