Is Uni V3 Lib Safe? — Trust Score: 47.5/100
According to Nerq's independent analysis of @aperture_finance/uni-v3-lib, this uncategorized has a trust score of 47.5 out of 100, earning a D grade. With 0 stars on npm_full, it is below the recommended threshold of 70. Compliance: 82/100 across 52 jurisdictions. Data sourced from 13+ independent signals including GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-19. Machine-readable data (JSON).
Is Uni V3 Lib safe?
NO — USE WITH CAUTION — Uni V3 Lib has a Nerq Trust Score of 47.5/100 (D). It has below-average trust signals with significant gaps in security, maintenance, or documentation. Not recommended for production use without thorough manual review and additional security measures.
Trust Assessment
Caution — @aperture_finance/uni-v3-lib has below-average trust signals. There may be concerns around maintenance frequency, security practices, or ecosystem adoption. Proceed with care and conduct additional due diligence.
Trust Signal Breakdown
Details
| Author | gnarlycow |
| Category | uncategorized |
| Stars | 0 |
| Source | https://www.npmjs.com/package/@aperture_finance/uni-v3-lib |
Regulatory Compliance
| EU AI Act Risk Class | Not assessed |
| Compliance Score | 82/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Community Reviews
No reviews yet. Be the first to review @aperture_finance/uni-v3-lib.
What Is Uni V3 Lib?
Uni V3 Lib is a AI tool in the uncategorized category. A suite of Solidity libraries that have been imported and rewritten from Uniswap's v3-core and v3-periphery
As of March 2026, Uni V3 Lib is available on npm_full, making it an emerging tool in the AI ecosystem. But popularity alone does not equal safety — which is why Nerq independently analyzes every tool across 13+ trust signals.
How Nerq Assesses Uni V3 Lib's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Uni V3 Lib performs in each:
- Compliance (82/100): Uni V3 Lib is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
The overall Trust Score of 47.5/100 (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 Uni V3 Lib?
Uni V3 Lib is designed for:
- Developers and teams working with uncategorized tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: We recommend caution with Uni V3 Lib. The low trust score suggests potential risks in security, maintenance, or community support. Consider using a more established alternative for any production or sensitive workload.
How to Verify Uni V3 Lib's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any AI tool:
- Check the source code — Review the repository 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 Uni V3 Lib's dependency tree. - Review permissions — Understand what access Uni V3 Lib requires. AI tools should follow the principle of least privilege.
- Test in isolation — Run Uni V3 Lib 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=@aperture_finance/uni-v3-lib - Review the license — Confirm that Uni V3 Lib'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 Uni V3 Lib
When evaluating whether Uni V3 Lib is safe, consider these category-specific risks:
Understand how Uni V3 Lib processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Uni V3 Lib's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Uni V3 Lib. Security patches and bug fixes are only effective if you're running the latest version.
If Uni V3 Lib 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 Uni V3 Lib's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Uni V3 Lib in violation of its license can expose your organization to legal liability.
Best Practices for Using Uni V3 Lib Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Uni V3 Lib while minimizing risk:
Periodically review how Uni V3 Lib is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Uni V3 Lib and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Uni V3 Lib only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Uni V3 Lib's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Uni V3 Lib is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Uni V3 Lib?
Even promising tools aren't right for every situation. Consider avoiding Uni V3 Lib 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 Uni V3 Lib's trust score of 47.5/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Uni V3 Lib Compares to Industry Standards
Nerq indexes over 204,000 AI agents and tools across dozens of categories. Among uncategorized tools, the average Trust Score is 62/100. Uni V3 Lib's score of 47.5/100 is below the category average of 62/100.
This suggests that Uni V3 Lib trails behind many comparable uncategorized tools. Organizations with strict security requirements should evaluate whether higher-scoring alternatives better meet their needs.
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 Uni V3 Lib 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, Uni V3 Lib'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 Uni V3 Lib's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=@aperture_finance/uni-v3-lib&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 Uni V3 Lib are strengthening or weakening over time.
Key Takeaways
- Uni V3 Lib has a Trust Score of 47.5/100 (D) and is not yet Nerq Verified.
- Uni V3 Lib has significant trust gaps. Consider higher-rated alternatives unless specific requirements mandate its use.
- Among uncategorized tools, Uni V3 Lib scores below 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.