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detecting-data-and-model-poisoning

Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction),

SKILL.md metadata excerpt. This is the source’s description, not an instruction to this site.

mukul975/anthropic-cybersecurity-skillsskills/detecting-data-and-model-poisoning/SKILL.md

Before you use it

Input
Not independently summarized. Check the source’s scope and examples.
Expected output
Not independently summarized; do not assume a ready-to-use artifact.
Prerequisites
Review the source instructions, scripts, accounts and tools in your intended environment.

Installation guidance is not individually established for this example. Read the source and confirm its requirements.

Publisher documentation, static file observations and environment tests are different kinds of evidence. Runtime behavior has not been observed.

Study the method

Start with the task scope and instructions, then inspect supporting files and examples. Compare the workflow with your own inputs, constraints and intended result.

5 files in the observed scope; 1 in scripts/, 2 in references/, 0 in assets/.

The supporting file list has not been read for this source. Open the source directory to inspect it.

Zero files in scripts/ does not mean no execution, network access or file writes. Markdown and other folders may describe or contain executable behavior.

Source version and requirements

Observed
2026-09-12T11:41:10.750Z
Source commit
54a798831d2266a3ca61ce68a7acb80b81160d57
Content SHA-256
be0338898f0fdcc1567905dece3b9479090463d18859de732677163c15f538d2
License
Apache-2.0
Author attribution
Repository owner: mukul975. Individual authorship is not independently established.
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This page describes a pinned source snapshot, not the latest repository state. Changes after this commit have not been reconciled here. A repository license does not necessarily cover external models, datasets or services.

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Reference note and manual copy