Is Agentlisa Safe?
Agentlisa — Nerq Trust Score 41.5/100 (E grade). Based on analysis of 3 trust dimensions, it is has notable safety concerns. Last updated: 2026-07-20.
Exercise caution with Agentlisa. Agentlisa is a software tool with a Nerq Trust Score of 41.5/100 (E), based on 3 independent data dimensions. Below the recommended threshold of 70. Maintenance: 0/100. Popularity: 0/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-07-20. Machine-readable data (JSON).
Is Agentlisa safe?
NO — USE WITH CAUTION — Agentlisa has a Nerq Trust Score of 41.5/100 (E). 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.
What is Agentlisa's trust score?
Agentlisa has a Nerq Trust Score of 41.5/100, earning a E grade. This score is based on 3 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Agentlisa?
Agentlisa's strongest signal is maintenance at 0/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Agentlisa and who maintains it?
| Author | 0xb5051843ea5de8815d16c40fdfdeecd8c7afa677 |
| Category | Security |
| Stars | 1 |
| Source | https://8004scan.io/agents/agentlisa |
| Protocols | x402 |
Popular Alternatives in security
What Is Agentlisa?
Agentlisa is a security tool: AgentLISA secures smart contracts at the speed of modern development.. It has 1 GitHub stars. Nerq Trust Score: 42/100 (E).
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 Agentlisa's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Agentlisa performs in each:
- Maintenance (0/100): Agentlisa is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (0/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Community (0/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 41.5/100 (E) 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 Agentlisa?
Agentlisa is designed for:
- Developers and teams working with security tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: We recommend caution with Agentlisa. 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 Agentlisa'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 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 Agentlisa's dependency tree. - Review permissions — Understand what access Agentlisa requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Agentlisa 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=AgentLISA - Review the license — Confirm that Agentlisa'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 Agentlisa
When evaluating whether Agentlisa is safe, consider these category-specific risks:
Understand how Agentlisa processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Agentlisa's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Agentlisa. Security patches and bug fixes are only effective if you're running the latest version.
If Agentlisa 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 Agentlisa's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Agentlisa in violation of its license can expose your organization to legal liability.
Best Practices for Using Agentlisa Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Agentlisa while minimizing risk:
Periodically review how Agentlisa is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Agentlisa and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Agentlisa only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Agentlisa's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Agentlisa is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Agentlisa?
Even promising tools aren't right for every situation. Consider avoiding Agentlisa 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 Agentlisa's trust score of 41.5/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Agentlisa Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among security tools, the average Trust Score is 67/100. Agentlisa's score of 41.5/100 is below the category average of 67/100.
This suggests that Agentlisa trails behind many comparable security 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 Agentlisa 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, Agentlisa'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 Agentlisa's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=AgentLISA&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 Agentlisa are strengthening or weakening over time.
Agentlisa vs Alternatives
In the security category, Agentlisa scores 41.5/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Agentlisa vs Ciphey — Trust Score: 62.2/100
- Agentlisa vs strix — Trust Score: 68.4/100
- Agentlisa vs SWE-agent — Trust Score: 67.2/100
Key Takeaways
- Agentlisa has a Trust Score of 41.5/100 (E) and is not yet Nerq Verified.
- Agentlisa has significant trust gaps. Consider higher-rated alternatives unless specific requirements mandate its use.
- Among security tools, Agentlisa scores below the category average of 67/100, 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
Is Agentlisa Safe?
What is Agentlisa's trust score?
What are safer alternatives to Agentlisa?
How often is Agentlisa's safety score updated?
Can I use Agentlisa in a regulated environment?
See Also
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.