Apakah Draggan Aman?
Draggan — Nerq Trust Score 59.6/100 (Nilai D). Skor berdasarkan 1 independent trust signals.
Draggan adalah software tool dengan Skor Kepercayaan Nerq sebesar 59.6/100 (D), based on 3 dimensi data independen. Data bersumber dari berbagai sumber publik termasuk registri paket, GitHub, NVD, OSV.dev, dan OpenSSF Scorecard. Terakhir diperbarui: n/a. Data yang dapat dibaca mesin (JSON).
Apakah Draggan Aman?
Rincian Skor Kepercayaan — Draggan has a Nerq Trust Score of 59.6/100 (D). Measured across 1 independent trust signal.
Berapa skor kepercayaan Draggan?
Draggan memiliki Skor Kepercayaan Nerq 59.6/100 dengan nilai D. Skor ini didasarkan pada 1 dimensi yang diukur secara independen.
Apa temuan keamanan utama untuk Draggan?
Sinyal terkuat Draggan adalah kepatuhan pada 100/100. Tidak ada kerentanan yang diketahui terdeteksi.
Apa itu Draggan dan siapa yang mengelolanya?
| Pembuat | DragGan |
| Kategori | Other |
| Bintang | 1,028 |
| Sumber | https://huggingface.co/spaces/DragGan/DragGan |
| Protocols | huggingface_hub |
Kepatuhan Regulasi
| EU AI Act Risk Class | Not assessed |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Alternatif Populer di other
What Is Draggan?
Draggan is a software tool in the other category with 1,028 GitHub stars. Nerq Trust Score: 60/100 (D).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including keamanan vulnerabilities, pemeliharaan activity, license kepatuhan, and adopsi komunitas.
How Nerq Assesses Draggan's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensi. Here is how Draggan performs in each:
- Compliance (100/100): Draggan is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
The overall Trust Score of 59.6/100 (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 Draggan?
Draggan is commonly evaluated by:
- Developers and teams working with other tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
How to read the signals: Draggan'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 Draggan's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Tinjau repository keamanan policy, open issues, and recent commits for signs of active pemeliharaan.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Draggan's dependency tree. - Ulasan permissions — Understand what access Draggan requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Draggan 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=DragGan - Tinjau license — Confirm that Draggan'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 keamanan concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Draggan
When evaluating whether Draggan is safe, consider these category-specific risks:
Understand how Draggan processes, stores, and transmits your data. Tinjau tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Draggan's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher keamanan risk.
Regularly check for updates to Draggan. Keamanan patches and bug fixes are only effective if you're running the latest version.
If Draggan 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 Draggan's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Draggan in violation of its license can expose your organization to legal liability.
Best Practices for Using Draggan Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Draggan while minimizing risk:
Periodically review how Draggan is used in your workflow. Check for unexpected behavior, permissions drift, and kepatuhan with your keamanan policies.
Ensure Draggan and all its dependencies are running the latest stable versions to benefit from keamanan patches.
Grant Draggan only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Draggan's keamanan advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Draggan is used within your organization, including data handling guidelines and acceptable use cases.
Situations That Warrant Independent Review of Draggan
Nerq's signals are one input. In the following situations, evaluate Draggan'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 Draggan's measured trust score of 59.6/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Draggan is suitable for any particular use.
How Draggan Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among other tools, the average Trust Score is 62/100. Draggan's score of 59.6/100 is near the category average of 62/100.
This places Draggan in line with the typical other 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 sedang 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 Draggan 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 pemeliharaan patterns change, Draggan'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 keamanan and quality. Conversely, a downward trend may signal reduced pemeliharaan, growing technical debt, or unresolved vulnerabilities. To track Draggan's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=DragGan&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 — keamanan, pemeliharaan, dokumentasi, kepatuhan, and community — has evolved independently, providing granular visibility into which aspects of Draggan are strengthening or weakening over time.
Draggan vs Alternatif
In the other category, Draggan scores 59.6/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Draggan vs cs-video-courses — Trust Score: 59.9/100
- Draggan vs awesome-scalability — Trust Score: 59.4/100
- Draggan vs superpowers — Trust Score: 62.4/100
Kesimpulan Utama
- Draggan has a measured Nerq Trust Score of 59.6/100 (D) — a composite of independent signals, not a suitability judgment.
- Among other tools, Draggan scores near the category average of 62/100 (a positional measurement relative to peers).
- The individual signals — keamanan, pemeliharaan, dokumentasi, kepatuhan, community — are shown above. Weigh them against your own requirements.
- Query the current measured values via Nerq's Preflight API.
Pertanyaan yang Sering Diajukan
Apakah Draggan Aman?
Berapa skor kepercayaan Draggan?
Apa alternatif yang lebih aman dari Draggan?
Seberapa sering skor keamanan Draggan diperbarui?
Bisakah saya menggunakan Draggan di lingkungan yang diatur?
Lihat juga
Disclaimer: Skor kepercayaan Nerq adalah penilaian otomatis berdasarkan sinyal yang tersedia secara publik. Ini bukan rekomendasi atau jaminan. Selalu lakukan verifikasi mandiri Anda sendiri.