DeepAudit: A Multi-Agent AI Code Auditor That Runs on Ollama

What DeepAudit Solves and Who It Is For

DeepAudit targets a specific pain point: manual code vulnerability mining is slow, expensive, and requires deep expertise. The project's pitch is that an AI team of multiple agents can do this work autonomously, and that the barrier to entry should be low enough for beginners. The README states: 'Everyone has an AI hacker team that makes vulnerability mining within reach.' It is aimed at developers, security researchers, and perhaps even hobbyists who want to scan codebases for flaws without hiring a pentest firm. The tool is designed for projects that can be imported from GitHub, GitLab, or Gitea, and it also accepts pasted code or uploaded files for immediate analysis. This makes it relevant for open-source maintainers who want a quick security pass, or for bug bounty hunters who need to triage many repositories. The emphasis on one-click deployment and Ollama support suggests a user base that may not have access to commercial cloud AI APIs or that wants to keep code and data on-premises.

The Multi-Agent Mechanism and the AI Audit Flow

The core of DeepAudit is a multi-agent system. The README mentions 'autonomous collaborative auditing' and shows a 'Multi-Agent depth audit' entry point in the UI. The architecture involves multiple AI agents that presumably take on different roles, such as code review, vulnerability hypothesis, and PoC generation. The system logs agent thinking and execution in real time, which is visible in the 'audit flow log' feature. This is not a single LLM call that returns a verdict; it is a pipeline where agents collaborate. The exact agent roles and the message-passing protocol are not detailed in the provided material, but the presence of a dashboard and project management implies a stateful workflow. The tool also includes a 'sandbox PoC verification' step, which means that when an agent finds a potential vulnerability, it can attempt to exploit it in a controlled environment to confirm it. This is a significant feature because many static analyzers stop at a report, leaving the user to verify manually. The README shows that the closed-source version has found real CVEs, but it is not clear if the open-source version has the same capability. The design choice to include a sandbox is a strong signal that the authors care about reducing false positives, but it also adds complexity and risk if the sandbox is not properly isolated.

Installation and First Run: Commands and Configuration

The README emphasizes 'one-click deployment and operation for beginners.' While the exact commands are not in the provided excerpt, the project is written in Python and uses FastAPI for the backend and React with TypeScript for the frontend, as indicated by the badges. Typical installation would involve cloning the repository, setting up a Python virtual environment, installing dependencies via pip, and then running a start script. The support for Ollama means that you can configure the system to use a local Ollama instance instead of a cloud API. This likely involves setting an environment variable or a config file to point to the Ollama endpoint, such as 'OLLAMA_HOST'. The README mentions 'Support transfer station,' which might refer to a proxy or relay for API calls. For a beginner, the one-click claim suggests that there is a script like 'start.sh' or 'docker-compose.yml' that launches the entire stack. Given the lack of explicit commands in the material, a user should check the repository's docs or the README_EN.md for the exact steps. The UI has an 'instant analysis' feature that allows pasting code or uploading files, so a quick test can be done without setting up a full project import.

The CVE and GHSA Track Record: What It Does and Does Not Prove

DeepAudit's README lists 49 CVEs and 6 GHSA advisories, all attributed to a 'closed-source version.' The table includes vulnerabilities in well-known projects like Zentao PMS, Dataease, PowerJob, JimuReport, H2O-3, and OpenClaw. The types range from SSRF to deserialization to privilege escalation, with CVSS scores up to 9.8. This is a strong claim. However, the material explicitly states that these findings come from an internal or closed version, not necessarily the open-source code you can clone today. This is a critical distinction. The open-source repository may have the same core, but the README does not confirm that the open version reproduces those results. For a user evaluating the tool, this track record is evidence of the approach's potential, but it is not a guarantee of current performance. It also raises a question: why are the CVE-finding capabilities not fully open? The project's AGPL-3.0 license applies to the open code, but the closed version might have additional proprietary components. This split is a limitation that users must accept.

A Genuine Limitation: The Open-Source vs. Closed-Source Gap

The most significant limitation is the uncertainty around the open-source version's capability. The README's CVE list is a marketing asset, but it is tied to a closed version. If you deploy the open-source DeepAudit, you may not get the same agent quality or the same sandbox reliability. The README also mentions 'quick mode' for reports, which suggests that the full agent mode is heavier and may require more computational resources. Another limitation is the sandbox itself: running PoC exploits is dangerous. If the sandbox is not properly isolated, it could execute malicious code on your host. The README does not provide details on the sandbox's isolation mechanism, so a user must assume some risk. For a beginner, the one-click claim might be misleading because setting up Ollama and a sandbox still requires some system administration knowledge. The tool is also Python-based, which means it may not be ideal for auditing projects in other languages, though the UI suggests it can import any project. The AGPL-3.0 license is another constraint: if you modify the tool and offer it as a service, you must release your changes. That is a real cost for commercial use.

A Real Alternative: Semgrep and Its Different Approach

A well-known alternative to DeepAudit is Semgrep, a static analysis tool that uses pattern matching and dataflow analysis to find vulnerabilities. Semgrep does not use AI agents; it relies on a set of rules that are either community-contributed or custom-written. The difference in approach is fundamental: Semgrep is deterministic and fast, scanning a codebase in seconds, but it can only find what its rules know. It produces a list of potential issues with high precision for common patterns, but it does not generate PoCs or reason about novel vulnerabilities. DeepAudit, on the other hand, uses LLM agents to reason about code, which can potentially find logic flaws that a rule-based tool would miss. However, LLM-based analysis is slower, more expensive, and less predictable. Semgrep is free and open-source under LGPL, which is more permissive than AGPL. If you need a quick, reliable scan for known vulnerability classes, Semgrep is a solid choice. If you need to find zero-day logic bugs and are willing to accept the uncertainty and resource cost, DeepAudit is the kind of tool that attempts that. The trade-off is clear: deterministic rules vs. probabilistic reasoning.

Maintenance, Upgrade Cost, and License Implications

The repository has recent releases (v3.0.2, v3.0.3, v3.0.4) with the latest from January 2026, indicating active maintenance. The default branch is v3.0.0, which is odd, but the release tags suggest a fast iteration cycle. This means users can expect updates, but also that the API or configuration may change between versions. Upgrade cost is not documented, but with a multi-agent system, model updates and dependency changes could require re-tuning. The project is licensed under AGPL-3.0, which is a strong copyleft license. If you use DeepAudit as part of a service, you must make your source code available to users. This is a significant consideration for commercial adoption. The license also affects how you can integrate the tool into your own products without open-sourcing them. The README mentions 'Support transfer station,' which might be a way to route API calls through a proxy, but that does not change the license obligations. Before deploying, a legal review of AGPL implications is advisable, but as a general note, AGPL is stricter than MIT or Apache.

Who Should Adopt It and What to Verify First

Adopt DeepAudit if you are a security researcher or a developer who wants to experiment with AI-driven vulnerability mining on your own projects, and you are comfortable with Python and running local models via Ollama. It is also suitable for teams that need a self-hosted solution to avoid sending code to external AI services. Do not adopt it if you need a proven, production-grade scanner with a clear SLA, or if you cannot accept the AGPL license for your use case. Before adopting, verify three things. First, test the open-source version on a small, known-vulnerable project to see if it matches the closed-source claims. Second, ensure that the sandbox PoC verification is isolated by checking the configuration for network access and containerization. Third, check the README_EN.md or the repository's docs for the exact installation commands, as the provided material does not include them. Also, confirm that the model you plan to use with Ollama is compatible with DeepAudit's agent prompts. The project's own CVE list is a strong signal, but it is not a substitute for your own validation.

Editorial conclusion

DeepAudit is for security researchers and developers who want an AI-driven multi-agent code auditor that can run locally via Ollama and produce PoC-verified reports. It is not for teams that need a fully audited, enterprise-grade tool or that cannot accept AGPL-3.0 obligations. Before adopting it, verify that the open-source version matches the capabilities claimed for the closed-source version, test it on a small non-critical project, and confirm that the sandbox PoC verification actually isolates execution. The project's own CVE list is impressive, but it is tied to a closed version, so treat the open repository as a starting point, not a proven product.

Official sources

1. Official README
2. Project repository
3. Release notes