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 cloudgit 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/cloud)<a href="https://agentmods.dev/skills/purpleailab/decepticon/cloud"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/cloud.svg" alt="Measured on agentmods" 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 31 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
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 | $0.00034 | $0.00477 |
| Opus 5 | $0.00017 | $0.00238 |
| Sonnet 5 | $0.00007 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
cloud-overview 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cloud — 89% identical, 12 lines differ
What it actually says
Cloud Hunter Skill Catalog
Playbooks
| Skill | Use for |
|---|---|
/skills/standard/cloud/aws-iam-enum/SKILL.md |
IAM enumeration + privesc |
/skills/standard/cloud/s3-takeover/SKILL.md |
Dangling bucket / subdomain takeover |
/skills/standard/cloud/k8s-pivot/SKILL.md |
Pod escape, RBAC abuse, hostPath |
/skills/standard/cloud/terraform-state-leak/SKILL.md |
Exposed state file exploitation |
/skills/standard/cloud/imds-pivot/SKILL.md |
SSRF → metadata → IAM role |
Workflow (authenticated engagement)
bash("aws sts get-caller-identity")bash("aws iam list-attached-user-policies --user-name <me>")- For each attached policy: fetch JSON and
iam_policy_audit - Feed Terraform state via
bash("aws s3 cp s3://bucket/terraform.tfstate -")→tfstate_audit bash("kubectl get pods -A -o json")→k8s_audit- Every privesc primitive → kg_add_node + chain edges
Workflow (post-SSRF)
metadata_endpoints("aws")for the target cloud- Pivot URL one at a time via the SSRF vector
- Confirmed creds →
credentialnode +leaksedge from the SSRF vuln plan_attack_chains(promote=True)to see the full path
What ships with it
19 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.
- aws-iam-enum/SKILL.md 2.5 KB
- aws-iam-passrole-chain/SKILL.md 5.9 KB
- azure-managed-identity/SKILL.md 4.8 KB
- container/container-cve/SKILL.md 4.6 KB
- container/docker-socket-mount/SKILL.md 3.8 KB
- container/k8s-pod-escape/SKILL.md 6.2 KB
- container/k8s-rbac-abuse/SKILL.md 6.5 KB
- container/SKILL.md 2.6 KB
- entra-conditional-access-bypass/SKILL.md 7.9 KB
- entra-device-code-phishing/SKILL.md 7.4 KB
- entra-enum/SKILL.md 7.7 KB
- entra-privesc/SKILL.md 11 KB
- gcp-org-escalation/SKILL.md 16 KB
- gcp-svc-account-impersonation/SKILL.md 5.2 KB
- imds-pivot/SKILL.md 6.9 KB
- k8s-pivot/SKILL.md 6.1 KB
- m365-mailbox-compromise/SKILL.md 14 KB
- s3-takeover/SKILL.md 5.0 KB
- terraform-state-leak/SKILL.md 6.6 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.
- 4d ago First seen · 40 lines · 34 tokens per session scan A adc29398cb7a
cloud-overview is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,452 stars, last pushed 8d ago), licensed Apache-2.0. It adds 34 tokens to every session and 477 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 2026-09-03.
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