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 t14-infra-warfaregit 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/t14-infra-warfare)<a href="https://agentmods.dev/skills/purpleailab/decepticon/t14-infra-warfare"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t14-infra-warfare/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/t14-infra-warfare"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/t14-infra-warfare.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.00840 |
| Opus 5 | $0.00021 | $0.00420 |
| Sonnet 5 | $0.00008 | $0.00168 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
aatmf-t14-infra-warfare 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
T14 — Infrastructure & Economic Warfare
Attacks aimed at degrading service availability, exhausting budget, or causing economic harm via abuse of the LLM endpoint. Adjacent to classical DoS but LLM-specific cost dynamics.
Techniques
T14.001 — Per-account budget exhaustion
- Multiple low-bandwidth requests each maximizing token cost
- Free-tier abuse via account creation farms
- Direct DDoS of paid endpoint (uses budget at attack rate)
Defenders: per-account caps + alerts.
T14.002 — Shared-quota poisoning
Multi-tenant LLM services share quotas at a provider level. Attacker exhausts the shared quota → all tenants degraded.
Targets: SaaS products that meter LLM access but share underlying API account.
T14.003 — GPU resource starvation
Self-hosted inference. Long-context queries hog GPU:
- Send queries near max context length
- Send batch of long-context queries in parallel
- Loop to maintain pressure
Cost-amplification ratio: 1 user request → 100% GPU utilization.
T14.004 — Cost amplification (T5.002 cross-ref)
Prompt engineering to maximize output cost:
- "Repeat 'token' 5000 times"
- "Generate the maximum-length response you're allowed to produce"
- Quadratic context patterns that bloat each turn
T14.005 — Pricing-tier exploit
Some APIs charge differently based on model variant. Trick the endpoint into using the expensive variant via parameter manipulation (T5.004 overlap).
T14.006 — Caching-pollution
Where LLM provider caches identical prompts (cost-reduction feature):
- Submit prompts that fill cache but produce uncacheable outputs
- Cache thrashing → no cost savings, full cost incurred
T14.007 — Streaming attack
Open many streaming requests → never consume → endpoint holds connection slots open until timeout.
Probe pattern
T14 is load-testing territory. Use k6, locust, hey:
# Spike test
hey -n 1000 -c 100 -m POST -H 'Authorization: Bearer X' \
-d '{"prompt":"'$(python3 -c 'print("a"*100000)')'"}' \
https://target/api/chat
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 · 106 lines · 42 tokens per session scan A 3c56f665c81c
aatmf-t14-infra-warfare is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,482 stars, last pushed 12d ago), licensed Apache-2.0. It adds 42 tokens to every session and 840 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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