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.
git clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWrote 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/agents/bdfinst/agentic-dev-team/redteam-evasion-analyzer)<a href="https://agentmods.dev/agents/bdfinst/agentic-dev-team/redteam-evasion-analyzer"><img src="https://agentmods.dev/badge/agents/bdfinst/agentic-dev-team/redteam-evasion-analyzer.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00046 | $0.00767 |
| Opus 5 | $0.00023 | $0.00383 |
| Sonnet 5 | $0.00009 | $0.00153 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
redteam-evasion-analyzer 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 2d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red-Team Evasion Analyzer
Synthesize probes 03/04/05 into a judgment: which adversarials are realistic (a fraud actor could actually submit them), which succeed because of model brittleness, and what defenses raise the cost of evasion.
Context needs: artifact-stream
Inputs
results/05_evasion.json(adversarial examples)results/03_sensitivity.json(feature influence rankings)results/04_boundaries.json(per-feature decision boundaries)
Output
results/05_evasion_analysis.md:
1. Realism assessment
For each low-scoring example in probe 05 (sorted by score ascending — worst first), rate realism 0-3:
- 0 (unrealistic): payload values no legitimate transaction could produce (negative amount, future timestamp, impossible geolocation)
- 1 (synthetic-looking): values inside valid ranges but statistically improbable (all features at the 90th percentile simultaneously)
- 2 (plausible): values matching known fraud profiles but caught by a reasonable rule engine
- 3 (realistic): values that look like legitimate traffic and would not trigger common rules — the dangerous category
Cite feature values from the payload to justify the rating.
2. Evasion mechanism
For the top 3 realistic adversarials, explain why the model scored them low:
- Exploits a feature the model over-weights?
- Straddles a decision boundary from probe 04?
- Combines features in a way training data did not cover (distribution shift)?
- Exploits a fail-open seen in probe 06?
Reference probe 03 sensitivity rankings + probe 04 boundaries explicitly.
3. Attack cost
One paragraph estimating how hard it is to craft such adversarials in the wild:
- Low cost: values exposed by error-mining in probe 02; attacker needs only endpoint + rate-limit tolerance.
- Medium cost: probe-03-style measurement; ~100 queries.
- High cost: extraction (probe 07) + optimization; ~10K queries.
4. Defensive recommendations
Concrete, ranked:
- Pre-scoring validation layer (which features, what ranges)
- Ensemble blend (catches distribution-shift evasions)
- Rate-limit by fingerprint (caps probe queries)
- Reduce sensitivity to top-N brittle features (list them)
- Drift monitor on input distribution
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.
- 2d ago First seen · 100 lines · 46 tokens per session scan A ced8cf4f4756
redteam-evasion-analyzer is an agent published in the GitHub repository bdfinst/agentic-dev-team (280 stars, last pushed 3d ago), licensed MIT. It adds 46 tokens to every session and 767 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 2026-09-05.
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