GraphCanon updated Oct 6, 2026 · GitHub synced Oct 6, 2026
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Decision brief
PARDEN defends AI models from adversarial attacks using repetition methods.
Good fit when
- When studying defenses against AI jailbreaks
- For evaluation on specific harmful strings generated by LLama2-7B
Avoid when
- If focusing on real-time defense implementation outside research settings
- For general model alignment without concern for adversarial attacks
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (874d since push)
- As of Oct 6, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 6, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/Ed-Zh/PARDENSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Contains data and code for PARDEN, which studies defending aligned language models from adversarial attacks
Capability facts
- Languages
- html
Source: github.language · Oct 6, 2026
Categories
Tags
README
This repo contains the data and code (minimal demo) for our paper (accepted at ICML 2024): PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition Paper: https://arxiv.org/abs/2405.07932 Blogpost: PARDEN data/ contains the benign and harmful datasets genearted by...
For agents
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