Individual skill
adversarial-ml-evasion
Craft adversarial examples that cause trained ML classifiers to misclassify at inference time — image recognition, malware detectors, IDS, spam filters.
packages/decepticon/decepticon/skills/standard/analyst/adversarial-ml-evasion/SKILL.md
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- Last checked
- Nov 8, 2026
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- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- cybersecurity Repository topic
- hacking Repository topic
- langchain Repository topic
- langgraph Repository topic
- llm Repository topic
- generative-ai Repository topic
- pentesting Repository topic
- pentest Repository topic
- engineering Derived category
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Read excerpt
# Adversarial ML Evasion
Adversarial examples are inputs modified with small, deliberate perturbations that reliably
cause a trained classifier to produce incorrect predictions. The perturbation is typically
imperceptible to humans or functionally irrelevant, while the model's decision boundary is
crossed. This is distinct from LLM jailbreaks: the target is a trained discriminative model
(CNN, gradient-boosted tree, SVM, LSTM) served via an API or embedded in a product.
> **Authorized use only.** All testing must be conducted against systems you own or have
> explicit written permission to assess. Generating adversarial inputs against production
> ML APIs without authorization may violate the CFAA and equivalent statutes.
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