GraphCanon updated 2w · GitHub synced 2w
Decision brief
AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python.
Good fit when
- Implementing robust defenses for enterprise-level AI projects with high-security requirements
- Enhancing the resilience of LLM deployments in sensitive or regulated environments
Avoid when
- Projects requiring light-weight solutions where multi-agent systems might introduce complexity overhead
- Environments without access to Python and its ecosystem, as AutoDefense depends on specific Python packages
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (201d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install AutoDefenseSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
AutoDefense provides a framework for protecting large language models (LLMs) from jailbreak attempts using multi-agent systems.
Capability facts
- Languages
- python
Source: github.language · Aug 5, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 5, 2026)
pip install vllm autogen pandas retry openaiSource link
Tags
README
Installation
pip install vllm autogen pandas retry openai
For agents
This page has a .md twin and JSON over the API.