Is Singh veilig?
Singh — Nerq Trust Score 50.2/100 (D-beoordeling). Score gebaseerd op 1 independent trust signals.
Singh is een software tool met een Nerq Vertrouwensscore van 50.2/100 (D), based on 3 onafhankelijke gegevensdimensies. Gegevens afkomstig van meerdere openbare bronnen waaronder pakketregisters, GitHub, NVD, OSV.dev en OpenSSF Scorecard. Laatst bijgewerkt: n/a. Machineleesbare gegevens (JSON).
Is Singh veilig?
Vertrouwensscore details — Singh has a Nerq Trust Score of 50.2/100 (D). Measured across 1 independent trust signal.
Wat is de vertrouwensscore van Singh?
Singh heeft een Nerq Trust Score van 50.2/100 met het cijfer D. Deze score is gebaseerd op 1 onafhankelijk gemeten dimensies, waaronder beveiliging, onderhoud en community-adoptie.
Wat zijn de belangrijkste beveiligingsbevindingen voor Singh?
Het sterkste signaal van Singh is naleving met 100/100. Er zijn geen bekende kwetsbaarheden gedetecteerd.
Wat is Singh en wie onderhoudt het?
| Ontwikkelaar | Manvir857 |
| Categorie | Uncategorized |
| Bron | https://huggingface.co/spaces/Manvir857/Singh |
| Protocols | huggingface_hub |
Naleving van regelgeving
| EU AI Act Risk Class | Not assessed |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdicties |
What Is Singh?
Singh is a software tool in the uncategorized category available on huggingface_space_full. Nerq Trust Score: 50/100 (D).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including beveiliging vulnerabilities, onderhoud activity, license naleving, and gemeenschapsacceptatie.
How Nerq Assesses Singh's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensies. Here is how Singh performs in each:
- Compliance (100/100): Singh is broadly compliant. Assessed against regulations in 52 jurisdicties including the EU AI Act, CCPA, and GDPR.
The overall Trust Score of 50.2/100 (D) is the weighted combination of these measured signals. It is a measurement, not a pass/fail or suitability judgment — weigh the individual signals against your own requirements.
Who Typically Evaluates Singh?
Singh is commonly evaluated by:
- Developers and teams working with uncategorized tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
How to read the signals: Singh's measured signals (the trust signals above) are shown above. These are measurements, not a suitability judgment — weigh each signal against the requirements of your own use case and risk tolerance.
How to Verify Singh's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Bekijk de repository beveiliging policy, open issues, and recent commits for signs of active onderhoud.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Singh's dependency tree. - Beoordeling permissions — Understand what access Singh requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Singh 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=Singh - Bekijk de license — Confirm that Singh'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 beveiliging concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Singh
When evaluating whether Singh is safe, consider these category-specific risks:
Understand how Singh processes, stores, and transmits your data. Bekijk de tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Singh's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher beveiliging risk.
Regularly check for updates to Singh. Beveiliging patches and bug fixes are only effective if you're running the latest version.
If Singh 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 Singh's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Singh in violation of its license can expose your organization to legal liability.
Best Practices for Using Singh Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Singh while minimizing risk:
Periodically review how Singh is used in your workflow. Check for unexpected behavior, permissions drift, and naleving with your beveiliging policies.
Ensure Singh and all its dependencies are running the latest stable versions to benefit from beveiliging patches.
Grant Singh only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Singh's beveiliging advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Singh is used within your organization, including data handling guidelines and acceptable use cases.
Situations That Warrant Independent Review of Singh
Nerq's signals are one input. In the following situations, evaluate Singh's measured signals against your own requirements before making a decision:
- Environments handling sensitive or regulated data (healthcare, finance, government)
- Mission-critical systems where downtime has significant business impact
- Deployments with strict regulatory requirements that must be independently validated
For each situation, compare Singh's measured trust score of 50.2/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Singh is suitable for any particular use.
How Singh Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among uncategorized tools, the average Trust Score is 62/100. Singh's score of 50.2/100 is below the category average of 62/100.
This suggests that Singh trails behind many comparable uncategorized tools. Organizations with strict beveiliging 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 matig 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 Singh 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 onderhoud patterns change, Singh'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 beveiliging and quality. Conversely, a downward trend may signal reduced onderhoud, growing technical debt, or unresolved vulnerabilities. To track Singh's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Singh&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 — beveiliging, onderhoud, documentatie, naleving, and community — has evolved independently, providing granular visibility into which aspects of Singh are strengthening or weakening over time.
Belangrijkste conclusies
- Singh has a measured Nerq Trust Score of 50.2/100 (D) — a composite of independent signals, not a suitability judgment.
- Among uncategorized tools, Singh scores below the category average of 62/100 (a positional measurement relative to peers).
- The individual signals — beveiliging, onderhoud, documentatie, naleving, community — are shown above. Weigh them against your own requirements.
- Query the current measured values via Nerq's Preflight API.
Veelgestelde vragen
Is Singh veilig?
Wat is de vertrouwensscore van Singh?
Wat zijn veiligere alternatieven voor Singh?
Hoe vaak wordt de beveiligingsscore van Singh bijgewerkt?
Kan ik Singh gebruiken in een gereguleerde omgeving?
Zie ook
Disclaimer: Nerq-vertrouwensscores zijn geautomatiseerde beoordelingen op basis van openbaar beschikbare signalen. Ze vormen geen aanbeveling of garantie. Voer altijd uw eigen verificatie uit.