Is Open Autoglm Safe?

Is Open Autoglm safe?

Trust Score Breakdown — Open Autoglm has a Nerq Trust Score of 72.9/100 (B). Measured across 5 independent trust signals.

What Is Open Autoglm?

Open Autoglm is a software tool in the coding category: An AI framework for automating smartphone tasks using voice commands.. It has 23,383 GitHub stars. Nerq Trust Score: 73/100 (B).

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 Open Autoglm's Safety

Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Open Autoglm performs in each:

• Security (1/100): Open Autoglm's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
• Maintenance (1/100): Open Autoglm 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 documentation, usage examples, and contribution guidelines.
• Compliance (100/100): Open Autoglm is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
• Community (1/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.

The overall Trust Score of 72.9/100 (B) 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 Open Autoglm?

Open Autoglm 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: Open Autoglm's measured signals (security 1/100, maintenance 1/100, documentation 1/100, community 1/100) 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 Open Autoglm's Safety Yourself

While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:

1. Check the source code — Review the repository's security policy, open issues, and recent commits for signs of active maintenance.
2. Scan dependencies — Use tools like npm audit, pip-audit, or snyk to check for known vulnerabilities in Open Autoglm's dependency tree.
3. Review permissions — Understand what access Open Autoglm requires. Software tools should follow the principle of least privilege.
4. Test in isolation — Run Open Autoglm in a sandboxed environment before granting access to production data or systems.
5. Monitor continuously — Use Nerq's API to set up automated trust checks: GET nerq.ai/v1/preflight?target=Open-AutoGLM
6. Review the license — Confirm that Open Autoglm'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.
7. 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 Open Autoglm

When evaluating whether Open Autoglm is safe, consider these category-specific risks:

Best Practices for Using Open Autoglm Safely

Whether you're an individual developer or an enterprise team, these practices will help you get the most from Open Autoglm while minimizing risk:

Situations That Warrant Independent Review of Open Autoglm

Nerq's signals are one input. In the following situations, evaluate Open Autoglm'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 Open Autoglm's measured trust score of 72.9/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Open Autoglm is suitable for any particular use.

How Open Autoglm 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. Open Autoglm's score of 72.9/100 is significantly above the category average of 62/100.

This places Open Autoglm in the top tier of coding tools that Nerq tracks. Tools scoring this far above average typically demonstrate mature security practices, consistent release cadence, and broad community adoption.

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 Open Autoglm 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, Open Autoglm'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 Open Autoglm's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Open-AutoGLM&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 Open Autoglm are strengthening or weakening over time.

Open Autoglm vs Alternatives

In the coding category, Open Autoglm scores 72.9/100. There are higher-scoring alternatives available. For a detailed comparison, see:

• Open Autoglm vs AutoGPT — Trust Score: 65.3/100
• Open Autoglm vs ollama — Trust Score: 64.4/100
• Open Autoglm vs langchain — Trust Score: 77.0/100

Key Takeaways

• Open Autoglm has a measured Nerq Trust Score of 72.9/100 (B) — a composite of independent signals, not a suitability judgment.
• Among coding tools, Open Autoglm scores significantly above the category average of 62/100 (a positional measurement relative to peers).
• The individual signals — security, maintenance, documentation, compliance, community — are shown above. Weigh them against your own requirements.
• Query the current measured values via Nerq's Preflight API.