Is Autonomous Code Fixer Safe?
Autonomous Code Fixer is a software tool with a Nerq Trust Score of 53.5/100 (D). It is below the recommended threshold of 70. Security: 0/100. Maintenance: 1/100. Popularity: 0/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-24. Machine-readable data (JSON).
Is Autonomous Code Fixer safe?
CAUTION — Autonomous Code Fixer has a Nerq Trust Score of 53.5/100 (D). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
Trust Score Breakdown
Key Findings
Details
| Author | dhruvagarwal29 |
| Category | coding |
| Stars | 1 |
| Source | https://github.com/dhruvagarwal29/Autonomous-Code-Fixer |
| Protocols | rest |
Regulatory Compliance
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
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What Is Autonomous Code Fixer?
Autonomous Code Fixer is a software tool in the coding category: A secure, multi-step AI Coding Agent built with the Google Gemini API's Function Calling capability.. It has 1 GitHub stars. Nerq Trust Score: 54/100 (D).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including security vulnerabilities, maintenance activity, license compliance, and community adoption.
How Nerq Assesses Autonomous Code Fixer's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Autonomous Code Fixer performs in each:
- Security (0/100): Autonomous Code Fixer's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
- Maintenance (1/100): Autonomous Code Fixer is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (1/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Compliance (100/100): Autonomous Code Fixer is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 53.5/100 (D) reflects the weighted combination of these signals. This is below the Nerq Verified threshold of 70. We recommend additional due diligence before production deployment.
Who Should Use Autonomous Code Fixer?
Autonomous Code Fixer is designed for:
- Developers and teams working with coding tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: Autonomous Code Fixer is suitable for development and testing environments. Before production deployment, conduct a thorough review of its security posture, review the specific trust signals above, and consider whether a higher-scored alternative meets your requirements.
How to Verify Autonomous Code Fixer's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Review the repository's security policy, open issues, and recent commits for signs of active maintenance.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Autonomous Code Fixer's dependency tree. - Review permissions — Understand what access Autonomous Code Fixer requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Autonomous Code Fixer in a sandboxed environment before granting access to production data or systems.
- Monitor continuously — Use Nerq's API to set up automated trust checks:
GET nerq.ai/v1/preflight?target=Autonomous-Code-Fixer - Review the license — Confirm that Autonomous Code Fixer's license is compatible with your intended use case. Pay attention to restrictions on commercial use, redistribution, and derivative works. Some AI tools use dual licensing or have separate terms for enterprise customers that differ from the open-source license.
- Check community signals — Look at the project's issue tracker, discussion forums, and social media presence. A healthy community actively reports bugs, contributes fixes, and discusses security concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Autonomous Code Fixer
When evaluating whether Autonomous Code Fixer is safe, consider these category-specific risks:
Understand how Autonomous Code Fixer processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Autonomous Code Fixer's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Autonomous Code Fixer. Security patches and bug fixes are only effective if you're running the latest version.
If Autonomous Code Fixer connects to external APIs or services, each integration point is a potential attack surface. Audit all third-party connections, verify that data shared with external services is minimized, and ensure that integration credentials are rotated regularly.
Verify that Autonomous Code Fixer's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Autonomous Code Fixer in violation of its license can expose your organization to legal liability.
Autonomous Code Fixer and the EU AI Act
Autonomous Code Fixer is classified as Minimal Risk under the EU AI Act. This is the lowest risk category, meaning it faces minimal regulatory requirements. However, transparency obligations still apply.
Nerq's compliance assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal compliance.
Best Practices for Using Autonomous Code Fixer Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Autonomous Code Fixer while minimizing risk:
Periodically review how Autonomous Code Fixer is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Autonomous Code Fixer and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Autonomous Code Fixer only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Autonomous Code Fixer's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Autonomous Code Fixer is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Autonomous Code Fixer?
Even promising tools aren't right for every situation. Consider avoiding Autonomous Code Fixer in these scenarios:
- Production environments handling sensitive customer data
- Regulated industries (healthcare, finance, government) without additional compliance review
- Mission-critical systems where downtime has significant business impact
For each scenario, evaluate whether Autonomous Code Fixer's trust score of 53.5/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Autonomous Code Fixer Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among coding tools, the average Trust Score is 62/100. Autonomous Code Fixer's score of 53.5/100 is near the category average of 62/100.
This places Autonomous Code Fixer in line with the typical coding tool tool. It meets baseline expectations but does not distinguish itself from peers on trust metrics.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderate in isolation may actually represent strong performance within a challenging category — or vice versa. Nerq's category-relative analysis helps teams make informed decisions by showing not just absolute quality, but how a tool ranks against its direct peers.
Trust Score History
Nerq continuously monitors Autonomous Code Fixer and recalculates its Trust Score as new data becomes available. Our scoring engine ingests real-time signals from source repositories, vulnerability databases (NVD, OSV.dev), package registries, and community metrics. When a new CVE is published, a major release ships, or maintenance patterns change, Autonomous Code Fixer's score is updated within 24 hours.
Historical trust trends reveal whether a tool is improving, stable, or declining over time. A tool that consistently maintains or improves its score demonstrates ongoing commitment to security and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Autonomous Code Fixer's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Autonomous-Code-Fixer&include=history
Nerq retains trust score snapshots at regular intervals, enabling trend analysis across weeks and months. Enterprise users can access detailed historical reports showing how each dimension — security, maintenance, documentation, compliance, and community — has evolved independently, providing granular visibility into which aspects of Autonomous Code Fixer are strengthening or weakening over time.
Autonomous Code Fixer vs Alternatives
In the coding category, Autonomous Code Fixer scores 53.5/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Autonomous Code Fixer vs AutoGPT — Trust Score: 74.7/100
- Autonomous Code Fixer vs ollama — Trust Score: 73.8/100
- Autonomous Code Fixer vs langchain — Trust Score: 86.4/100
Key Takeaways
- Autonomous Code Fixer has a Trust Score of 53.5/100 (D) and is not yet Nerq Verified.
- Autonomous Code Fixer shows moderate trust signals. Conduct thorough due diligence before deploying to production environments.
- Among coding tools, Autonomous Code Fixer scores near the category average of 62/100, suggesting room for improvement relative to peers.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.