Aimer-zero

Aimer-zero / redforge-ai

Public

Open-core AI red teaming and offensive AI security evaluation platform.

22
4
69% credibility
Found May 31, 2026 at 22 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

RedForge AI is a security testing framework that helps authorized teams evaluate AI applications, RAG systems, and AI agents for vulnerabilities like prompt injection, tool abuse, and memory poisoning. It runs scoped campaigns, records detailed evidence, and generates reports developers can use to improve security.

How It Works

1
🔍 Discover RedForge AI

You hear about a security testing tool for AI applications that helps find weaknesses before attackers do.

2
📦 Install and explore

You download the tool and run the quick demo to see how it works with a safe practice target.

3
🎯 Set up your test scope

You define exactly which AI system to test, what areas to check, and how much testing to do.

4
🚀 Run your security campaign

The tool automatically tests your AI application using different techniques to probe for vulnerabilities.

5
📊 Review your findings

You see a clear report showing what was tested, what worked, and what needs attention, with evidence for each finding.

✅ Get actionable insights

You have a detailed report with specific recommendations to improve your AI system's security.

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Star Growth

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AI-Generated Review

What is redforge-ai?

RedForge AI is a security testing harness for AI systems. It runs controlled attack campaigns against LLM applications, RAG pipelines, agents with tools, and multi-agent setups, then produces replayable evidence reports. Built in Python with a CLI and FastAPI service, it lets you fire payloads from a catalog, observe what the target does, and generate findings you can act on.

Why is it gaining traction?

The space lacks structured red-teaming tooling that goes beyond jailbreak prompt lists. RedForge fills that gap with scoped targets, attack budgets, and evidence traces that survive beyond a single session. You can point it at a local demo agent, an OpenAI-compatible API, or a custom HTTP endpoint, and it handles authentication, CSRF, and multi-turn conversations. The built-in vulnerable agent gives you a safe target to test against before touching production systems.

Who should use this?

Security engineers evaluating AI products before launch. ML teams running regression tests on model updates. Red teams that need replayable evidence for compliance or client handoffs. It is not a turnkey scanner -- you need to understand your attack surface and configure scopes properly. Organizations without dedicated AI security expertise may find the learning curve steep relative to simpler evaluation tools.

Verdict

The framework shows solid architecture and thoughtful scope controls, but the credibility score of 0.699999988079071% and low star count reflect an early-stage project with limited community validation. The API is feature-rich and the evidence-first approach is sound, but documentation and test coverage appear thin. Approach with caution for production use; treat it as a capable prototype that needs more bake time before trusting with sensitive evaluations.

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