GraphCanon updated Oct 6, 2026 · GitHub synced Oct 6, 2026
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Decision brief
LLMFuzzer is an open-source fuzzing framework tailored for testing the robustness of Large Language Models through their APIs.
Good fit when
- When ensuring custom LLM integrations are secure against unexpected inputs and edge cases
- During development phases of apps that heavily rely on LLMs for critical functionalities
Avoid when
- If the project exclusively uses proprietary closed-source models without accessible APIs
- For general software testing not involving interactions with or security checks of language models
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (966d since push)
- As of Oct 6, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 6, 2026
- Security (OSV)
- 31 low (31 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install LLMFuzzerSimilar 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
LLMFuzzer is an open-source fuzzing framework designed specifically for testing Large Language Models (LLMs) and their integrations via LLM APIs.
Capability facts
- Languages
- python
Source: github.language · Oct 6, 2026
Categories
Tags
README
💼 License LLMFuzzer is licensed under the MIT License. See the LICENSE file for more details.
For agents
This page has a .md twin and JSON over the API.