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 mass-assignmentgit 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/mass-assignment)<a href="https://agentmods.dev/skills/purpleailab/decepticon/mass-assignment"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/mass-assignment/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/mass-assignment"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/mass-assignment.svg" alt="Reviewed on agentmods" width="80" 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 100 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00028 | $0.01250 |
| Opus 5 | $0.00014 | $0.00625 |
| Sonnet 5 | $0.00006 | $0.00250 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
mass-assignment scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
r = requests.post(f"{TARGET}/register", json={ Copies of this mod
1 near-identical copy found in the catalogue:
- mass-assignment — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mass Assignment + ORM Leak
API endpoints that bind request JSON straight to model update() /
create() without an allowlist can be coerced into setting admin
fields (is_admin, role, verified, etc).
1. Detect
# Capture a legitimate update request
PATCH /api/users/me
{"name": "Alice"}
# Try adding common privileged fields
PATCH /api/users/me
{"name": "Alice", "is_admin": true, "role": "admin", "verified": true,
"balance": 999999, "permissions": ["*"], "isStaff": true,
"membership_level": "premium", "tier": "enterprise"}
Re-fetch own profile. If any of the injected fields persists with attacker-set value → mass assignment.
2. Common field names to try
is_admin isAdmin admin superuser is_staff isStaff staff
role roles permission permissions scope scopes group groups
verified email_verified isVerified approved banned is_banned
balance credits points reputation
tier membership_level plan subscription
created_at email user_id uuid external_id organization_id
password password_hash
3. Framework-specific patterns
Rails (legacy, pre-strong-params)
User.create(params[:user]) → all params mass-assigned. Rails 4+ enforces
params.require(:user).permit(:name, :email). If permit list is too broad,
mass assignment.
Django REST Framework
UserSerializer(instance, data=request.data, partial=True).save() → all
declared fields settable. Bug: developer adds is_staff to serializer
fields by mistake.
Express / Mongoose
User.findOneAndUpdate({_id: req.params.id}, req.body)
// → any req.body field becomes a $set. Catastrophic.
Spring Boot
@RequestBody User u → Jackson binds all settable properties. If User
has setter for role, attacker can set it.
Go / Echo / Gin
c.Bind(&user) → same pattern.
4. ORM Leak — the response side
Sometimes the response serializer leaks fields the request can't set:
GET /api/users/123
{
"id": 123,
"name": "Alice",
"email": "[email protected]",
"password_hash": "$2a$12$...", ← leak!
"totp_secret": "JBSW...", ← leak!
"stripe_customer_id": "cus_...",
"internal_notes": "VIP customer"
}
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.
- 9d ago First seen · 154 lines · 28 tokens per session scan A 6a4f73412020
mass-assignment is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,491 stars, last pushed 13d ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,250 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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