mrwadams/stride-gpt

An AI-powered threat modeling tool that leverages OpenAI's GPT models to generate threat models for a given application based on the STRIDE methodology.

What it solves

STRIDE GPT automates the complex process of threat modeling. It helps developers and security teams identify potential vulnerabilities, map them to standardized adversary techniques, and generate actionable mitigations without requiring manual, time-consuming analysis of application architectures.

How it works

It uses Large Language Models (LLMs) to analyze application details, codebase repositories, or architecture diagrams. You provide the system context—such as authentication methods, data sensitivity, or a visual diagram—and the tool applies the STRIDE methodology to generate threat models, attack trees, and Gherkin test cases. It can operate as an autonomous agent that explores codebases, maps findings to MITRE ATT&CK or ATLAS frameworks, and outputs results in multiple formats like Markdown, JSON, and SARIF.

Who it’s for

This tool is designed for security engineers, developers, and architects who need to perform threat modeling on traditional applications, GenAI systems, or agentic AI applications and want to integrate security analysis into their existing development workflows.

Highlights

  • Agentic Analysis: Autonomous codebase exploration that plans, analyzes subsystems, and synthesizes findings.
  • Standardized Mapping: Automatically annotates threats with MITRE ATT&CK and ATLAS adversary technique IDs.
  • Flexible Inputs: Supports text descriptions, GitHub repository analysis, and architecture diagrams via an embedded draw.io editor or image uploads.
  • Comprehensive Reporting: Generates threat models, attack trees, DREAD risk scores, and Gherkin test cases in Markdown, HTML, JSON, and SARIF formats.
  • Broad Model Support: Compatible with various LLM providers (OpenAI, Anthropic, Google, Mistral, DeepSeek, and local models) via LiteLLM.
  • Interactive Experience: Offers both a full-featured CLI with a REPL and a user-friendly Streamlit web interface.

Related

  • Project
    Trampoline-AI/predict-rlm

    predict-rlm is a Python library that gives large language models a sandboxed, stateful runtime. Models write and execute Python code, keep mutable state, call focused sub‑models, and use tools/skills (e.g., PDF handling, spreadsheets). The framework records a full trace for observability and can feed those traces to optimisation tools like GEPA. It’s aimed at complex, multi‑step workflows such as document analysis, audit, or spreadsheet automation, and supports optional Docker sandboxing and rich callbacks.

  • Project
    CyberStrikeus/CyberStrike

    CyberStrike is an open‑source AI agent that turns any LLM (Claude, GPT, Gemini, etc.) into an autonomous penetration‑testing platform. It adds a security‑focused “intelligence layer”, ships 13+ specialised agents and 7,600+ signed security‑skill files, supports 150+ AI providers (including offline models), and can orchestrate real tools locally or on remote Bolt servers. A TUI, optional web UI, and built‑in HackBrowser complete the workflow.

  • Project
    Kritt-ai/open-kritt

    open·kritt is an open‑source, self‑hosted platform that orchestrates AI agents (via Codex, Claude Code, Grok Build, etc.) to run custom, prompt‑driven security workflows on code repositories. It builds focused tasks, runs them in parallel Docker containers, validates findings, de‑duplicates and ranks them, and exports a complete report bundle. Designed for bug‑bounty researchers and DevSecOps, it offers a web UI and a headless CLI, supports any model provider, and is released under AGPL‑3.0.

  • Project
    vllm-project/guidellm

    GuideLLM is a Python library (also available as a container) that benchmarks LLM inference against real‑world workloads. It drives configurable traffic patterns to OpenAI‑compatible or vLLM servers, supports synthetic and real multimodal datasets, records detailed latency/token metrics, and outputs JSON/CSV/HTML reports for capacity planning and SLO verification.

  • Project
    WecoAI/aideml

    AIDE ML is an open‑source Python package that implements an LLM‑guided tree‑search agent for automatically writing, debugging, and benchmarking machine‑learning code. Users specify a dataset, a natural‑language goal, and an evaluation metric; the agent iteratively generates and refines Python scripts, pruning the search tree based on metric feedback. The repo includes a CLI, a Streamlit UI, HTML visualisation, Docker and local‑LLM support, and is designed for both research on agent architectures and practical ML pipeline generation.