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 adversary-emulationgit 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/adversary-emulation)<a href="https://agentmods.dev/skills/purpleailab/decepticon/adversary-emulation"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/adversary-emulation/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/adversary-emulation"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/adversary-emulation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to critical
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 →
- critical Prompt Injection · line 83 This content may contain harmful instructions that could cause physical harm if followed. CRITICAL: Review carefully before use.Fix: Remove all content that could lead to harmful outcomes. Add safety guardrails and human oversight for any high-risk operations.
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 | $0.00050 | $0.01888 |
| Opus 5 | $0.00025 | $0.00944 |
| Sonnet 5 | $0.00010 | $0.00378 |
| Haiku 4.5 | $0.00005 | $0.00189 |
Grade A, and why
adversary-emulation 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 10d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversary Emulation — index & methodology
Adversary emulation reproduces a specific, named threat actor's tactics, techniques and procedures (TTPs) — drawn from real, attributed intelligence — to test whether the target's people, process, and tooling detect and respond the way they should. It is distinct from generic penetration testing (opportunistic) and from simulation (abstract/random): emulation is threat-informed — every action traces to something a real group has been documented doing, mapped to MITRE ATT&CK.
Authorized use only. Emulate an actor's TTPs only within the engagement's Rules of Engagement and approved scope. Destructive techniques (Impact tactic: ransomware, wipers, ICS manipulation) are emulated as non-destructive proofs (e.g., a benign canary file, a dry-run) unless the RoE explicitly authorizes otherwise. The goal is to measure detection, not to cause damage.
When to use this
- The operator (or
roe.jsonthreat profile) names a specific actor to emulate, or names a sector/region whose dominant threat is a known group. - A purple-team / ATT&CK-evaluation engagement: run an actor's TTP chain while the
blue cell measures detection (see
kill-chain-analysisand theblue_cell). - You want a realistic, defensible attack plan instead of an ad-hoc one.
Methodology (5 steps)
- Select the actor. Map the engagement's industry/region/crown-jewels to a
relevant group (see the catalog below). When unsure, ask the operator via
ask_user_question. Record the choice in the OPPLAN. - Load the profile.
load_skill <slug>(e.g.load_skill apt29-cozy-bear). Each profile carries attribution, targeting, dated campaigns, the actor's TTPs mapped to ATT&CK technique IDs, signature tooling, emulation guidance (how to reproduce each TTP with Decepticon's own tools), and detection notes. - Scope to RoE. Intersect the actor's TTPs with the approved scope. Drop or down-scope anything out of bounds (e.g., replace a real wiper with a canary). Forbidden-destination / out-of-scope checks still apply at tool-call time.
- Emulate in kill-chain order. Walk Initial Access → Execution → Persistence → Priv-Esc → Defense Evasion → Credential Access → Discovery → Lateral Movement → Collection → C2 → Exfiltration → (proof-of) Impact, using only the techniques this actor is known for. Cite the ATT&CK ID in each finding.
- Measure & report. Record which actions the blue cell detected/blocked vs.
missed (
kill-chain-analysis, MTTD), and produce a threat-informed report that ties each result to the emulated actor + ATT&CK technique. Feeds the final report.
What ships with it
24 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- apt10-stone-panda/SKILL.md 26 KB
- apt28-fancy-bear/SKILL.md 21 KB
- apt29-cozy-bear/SKILL.md 19 KB
- apt33-elfin/SKILL.md 16 KB
- apt34-oilrig/SKILL.md 19 KB
- apt36-transparent-tribe/SKILL.md 24 KB
- apt37-reaper/SKILL.md 24 KB
- apt41-double-dragon/SKILL.md 20 KB
- dark-caracal/SKILL.md 23 KB
- fin7-carbanak/SKILL.md 22 KB
- kimsuky/SKILL.md 32 KB
- lazarus-group/SKILL.md 19 KB
- muddywater/SKILL.md 34 KB
- mustang-panda/SKILL.md 33 KB
- patchwork/SKILL.md 27 KB
- pink-sandstorm/SKILL.md 22 KB
- references/anyrun-free-iocs.md 17 KB
- references/cve-apt-mapping.md 14 KB
- salt-typhoon/SKILL.md 31 KB
- sandworm-team/SKILL.md 19 KB
- scattered-spider/SKILL.md 20 KB
- sidewinder/SKILL.md 22 KB
- turla/SKILL.md 40 KB
- volt-typhoon/SKILL.md 18 KB
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.
- 10d ago First seen · 89 lines · 50 tokens per session scan A 3345a6da363e
adversary-emulation is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,471 stars, last pushed 10d ago), licensed Apache-2.0. It adds 50 tokens to every session and 1,888 once invoked, about $0.0003 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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