Synexia Llmcpp è sicuro?
Synexia Llmcpp — Nerq Trust Score 66.0/100 (Grado C). Punteggio basato su 5 independent trust signals.
Synexia Llmcpp è un software tool con un Punteggio di fiducia Nerq di 66.0/100 (C), based on 5 dimensioni di dati indipendenti. Sicurezza: 0/100. Manutenzione: 1/100. Popolarità: 0/100. Dati provenienti da molteplici fonti pubbliche tra cui registri di pacchetti, GitHub, NVD, OSV.dev e OpenSSF Scorecard. Ultimo aggiornamento: n/a. Dati leggibili dalle macchine (JSON).
Synexia Llmcpp è sicuro?
Dettagli punteggio di fiducia — Synexia Llmcpp has a Nerq Trust Score of 66.0/100 (C). Measured across 5 independent trust signals.
Qual è il punteggio di fiducia di Synexia Llmcpp?
Synexia Llmcpp ha un Nerq Trust Score di 66.0/100 con voto C. Questo punteggio si basa su 5 dimensioni misurate indipendentemente, tra cui sicurezza, manutenzione e adozione della community.
Quali sono i risultati di sicurezza chiave per Synexia Llmcpp?
Il segnale più forte di Synexia Llmcpp è conformità a 100/100. Non sono state rilevate vulnerabilità note.
Cos'è Synexia Llmcpp e chi lo mantiene?
| Autore | hsoliwal |
| Categoria | Coding |
| Fonte | https://github.com/hsoliwal/synexia-llmcpp |
| Frameworks | openai · huggingface |
| Protocols | rest |
Conformità normativa
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Alternative popolari in coding
What Is Synexia Llmcpp?
Synexia Llmcpp is a software tool in the coding category: Self-contained CPU LLM coding agent based on llama.cpp fork.. Nerq Trust Score: 66/100 (C).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including sicurezza vulnerabilities, manutenzione activity, license conformità, and adozione della comunità.
How Nerq Assesses Synexia Llmcpp's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensioni. Here is how Synexia Llmcpp performs in each:
- Sicurezza (0/100): Synexia Llmcpp's sicurezza posture is poor. This score factors in known CVEs, dependency vulnerabilities, sicurezza policy presence, and code signing practices.
- Manutenzione (1/100): Synexia Llmcpp 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 documentazione, usage examples, and contribution guidelines.
- Compliance (100/100): Synexia Llmcpp is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Basato su GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 66.0/100 (C) 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 Synexia Llmcpp?
Synexia Llmcpp is commonly evaluated by:
- Developers and teams working with coding tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
How to read the signals: Synexia Llmcpp's measured signals (sicurezza 0/100, manutenzione 1/100, documentazione 1/100, community 0/100) 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 Synexia Llmcpp's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Controlla repository's sicurezza policy, open issues, and recent commits for signs of active manutenzione.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Synexia Llmcpp's dependency tree. - Recensione permissions — Understand what access Synexia Llmcpp requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Synexia Llmcpp 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=synexia-llmcpp - Controlla license — Confirm that Synexia Llmcpp'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 sicurezza concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Synexia Llmcpp
When evaluating whether Synexia Llmcpp is safe, consider these category-specific risks:
Understand how Synexia Llmcpp processes, stores, and transmits your data. Controlla tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Synexia Llmcpp's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher sicurezza risk.
Regularly check for updates to Synexia Llmcpp. Sicurezza patches and bug fixes are only effective if you're running the latest version.
If Synexia Llmcpp 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 Synexia Llmcpp's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Synexia Llmcpp in violation of its license can expose your organization to legal liability.
Synexia Llmcpp and the EU AI Act
Synexia Llmcpp 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 conformità assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal conformità.
Best Practices for Using Synexia Llmcpp Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Synexia Llmcpp while minimizing risk:
Periodically review how Synexia Llmcpp is used in your workflow. Check for unexpected behavior, permissions drift, and conformità with your sicurezza policies.
Ensure Synexia Llmcpp and all its dependencies are running the latest stable versions to benefit from sicurezza patches.
Grant Synexia Llmcpp only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Synexia Llmcpp's sicurezza advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Synexia Llmcpp is used within your organization, including data handling guidelines and acceptable use cases.
Situations That Warrant Independent Review of Synexia Llmcpp
Nerq's signals are one input. In the following situations, evaluate Synexia Llmcpp'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 Synexia Llmcpp's measured trust score of 66.0/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Synexia Llmcpp is suitable for any particular use.
How Synexia Llmcpp 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. Synexia Llmcpp's score of 66.0/100 is above the category average of 62/100.
This positions Synexia Llmcpp favorably among coding tools. While it outperforms the average, there is still room for improvement in certain trust dimensioni.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderato 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 Synexia Llmcpp 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 manutenzione patterns change, Synexia Llmcpp'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 sicurezza and quality. Conversely, a downward trend may signal reduced manutenzione, growing technical debt, or unresolved vulnerabilities. To track Synexia Llmcpp's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=synexia-llmcpp&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 — sicurezza, manutenzione, documentazione, conformità, and community — has evolved independently, providing granular visibility into which aspects of Synexia Llmcpp are strengthening or weakening over time.
Synexia Llmcpp vs Alternative
In the coding category, Synexia Llmcpp scores 66.0/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Synexia Llmcpp vs AutoGPT — Trust Score: 65.3/100
- Synexia Llmcpp vs ollama — Trust Score: 64.4/100
- Synexia Llmcpp vs langchain — Trust Score: 77.0/100
Punti chiave
- Synexia Llmcpp has a measured Nerq Trust Score of 66.0/100 (C) — a composite of independent signals, not a suitability judgment.
- Among coding tools, Synexia Llmcpp scores above the category average of 62/100 (a positional measurement relative to peers).
- The individual signals — sicurezza, manutenzione, documentazione, conformità, community — are shown above. Weigh them against your own requirements.
- Query the current measured values via Nerq's Preflight API.
Domande frequenti
Synexia Llmcpp è sicuro?
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Vedi anche
Disclaimer: I punteggi di fiducia Nerq sono valutazioni automatizzate basate su segnali disponibili pubblicamente. Non costituiscono raccomandazioni o garanzie. Effettua sempre la tua verifica personale.