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 adriannoes/awesome-agentic-ai --skill detecting-data-and-model-poisoninggit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning/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/adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-data-and-model-poisoning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 Memory Poisoning · line 30 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 76 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00023 | $0.02422 |
| Opus 5 | $0.00012 | $0.01211 |
| Sonnet 5 | $0.00005 | $0.00484 |
| Haiku 4.5 | $0.00002 | $0.00242 |
Grade A, and why
detecting-data-and-model-poisoning 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 8d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting Data and Model Poisoning
Authorized-use-only notice: This skill includes routines that craft poisoned samples and backdoor triggers for defensive validation. Generate and use poisoned data and backdoored models only in isolated test environments you control. Never deploy a backdoored model or distribute poisoned datasets.
Overview
Data poisoning and model backdooring attack the integrity of an ML system at training time rather than at inference. In data poisoning (MITRE ATLAS AML.T0020 Poison Training Data), an adversary injects manipulated samples into the training, fine-tuning, or RAG corpus so the resulting model misbehaves — degraded accuracy, targeted misclassification, or an attacker-chosen bias. In model backdooring (MITRE ATLAS AML.T0018 Backdoor ML Model), the model behaves normally on clean inputs but produces an attacker-chosen output whenever a hidden trigger (a pixel patch, a rare token, a phrase) is present. Both are amplified by ML supply-chain compromise (AML.T0010): poisoned public datasets, trojaned pre-trained weights downloaded from a hub, or a malicious model serialization. This is OWASP LLM04:2025 Data and Model Poisoning.
Detection spans the pipeline. On the data side: provenance and integrity checks, statistical outlier and label-flip detection, and de-duplication of suspiciously near-identical samples. On the model side: activation-clustering and spectral-signature analysis (which exploit the fact that poisoned samples activate the network differently than clean ones) and trigger reconstruction. On the supply-chain side: verifying weights hashes/signatures and refusing unsafe serialization formats (pickle-based .bin/.pt) in favor of safetensors. This skill implements all three using IBM's Adversarial Robustness Toolbox (ART), Cleanlab for label-quality issues, and integrity tooling.
When to Use
- Before training/fine-tuning on third-party or user-contributed data.
- Before deploying a model built on a downloaded pre-trained checkpoint.
- During an ML supply-chain security review.
- When investigating anomalous model behavior tied to specific inputs (possible backdoor trigger).
- As a CI/CD gate that scans datasets and model artifacts before they enter the pipeline.
What ships with it
4 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.
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.
- 8d ago First seen · 216 lines · 23 tokens per session scan A a04672e7f8bf
detecting-data-and-model-poisoning is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 23 tokens to every session and 2,422 once invoked, about $0.0001 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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