Home/Compare/Open-Prompt-Injection vs MGDebugger

Comparison

Open-Prompt-Injection vs MGDebugger

Verdict

Pick Open-Prompt-Injection if open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs; pick MGDebugger if mGDebugger offers hierarchical debugging for various levels of code granularity, emphasizing efficient error resolution and improved debug accuracy.

Markdown twin · Open-Prompt-Injection alternatives · MGDebugger alternatives

GraphCanon updated Oct 7, 2026

Open-Prompt-Injection logo

Open-Prompt-Injection

liu00222/Open-Prompt-Injection

★ 503pushed Sep 27, 2026
vs
MGDebugger logo

MGDebugger

YerbaPage/MGDebugger

★ 101pushed Jul 6, 2025

Trust & integrity

SignalOpen-Prompt-InjectionMGDebugger
Maintenance
Very active (6d since push)
As of Oct 4, 2026 · github_public_v1
Dormant (458d since push)
As of Oct 7, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Oct 4, 2026 · github_public_v1
Not a fork · Personal account
As of Oct 7, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Jul 11, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

Open-Prompt-Injection
Benchmark and toolkit for prompt injection attacks and defenses in LLMs
MGDebugger
Multi-Granularity LLM Debugger

Stars

Open-Prompt-Injection
503
MGDebugger
101

Forks

Open-Prompt-Injection
81
MGDebugger
10

Open issues

Open-Prompt-Injection
2
MGDebugger
1

Language

Open-Prompt-Injection
Python
MGDebugger
Python

Adopt for

Open-Prompt-Injection
Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs.
MGDebugger
MGDebugger offers hierarchical debugging for various levels of code granularity, emphasizing efficient error resolution and improved debug accuracy.

Persona

Open-Prompt-Injection
-
MGDebugger
-

Runtime

Open-Prompt-Injection
-
MGDebugger
-

License

Open-Prompt-Injection
MIT
MGDebugger
MIT

Last pushed

Open-Prompt-Injection
Sep 27, 2026
MGDebugger
Jul 6, 2025

Categories

Open-Prompt-Injection
Evaluation & Observability, LLM Frameworks
MGDebugger
Evaluation & Observability, LLM Frameworks

Trust and health

Maintenance

Open-Prompt-Injection
Very active (96%)
MGDebugger
Dormant (18%)

Days since push

Open-Prompt-Injection
6d
MGDebugger
458d

Open issues (now)

Open-Prompt-Injection
2
MGDebugger
1

Stars delta

Open-Prompt-Injection
+33 (30d)
MGDebugger
0 (30d)

Open issues delta

Open-Prompt-Injection
-12 (30d)
MGDebugger
+1 (30d)

OSV dependency advisories

Open-Prompt-Injection
No lockfile (source not queried)
MGDebugger
Published findings

Full report

Open-Prompt-Injection
Trust report
MGDebugger
Trust report

Shared compatibility

  • Python · Open-Prompt-Injection: Python runtime · MGDebugger: Python runtime

Choose Open-Prompt-Injection if…

  • Tags unique to Open-Prompt-Injection: llm-security, prompt-injection, security-and-privacy.
  • You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
  • More GitHub stars (503 vs 101) - visibility, not fit.

When NOT to use Open-Prompt-Injection

  • You require broader, more generalized security features not centered on prompt injection attacks.
  • Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.

Choose MGDebugger if…

  • Pricing: MGDebugger is free to use under MIT license but may require users to manage model hosting costs and dependencies..
  • Requirements: Min 4 GB RAM; Requires Python version 3.8 or later; vLLM version 0.6.0 or later must be installed for model inference.
  • Tags unique to MGDebugger: automatic-program-repair, code-generation, debugger, large-language-models.
  • When you need to perform granular analysis on complex codes, progressing from subfunctions to the whole system to ensure precise error detection and correction.

When NOT to use MGDebugger

  • Avoid using MGDebugger if you operate primarily on Mac systems and do not require support for quantized models (as some essential dependencies are unsupported on MacOS).
  • If your model does not align well with the DeepSeek-Coder-V2-Lite-Instruct or similar models, since the effectiveness of MGDebugger might vary without support for those particular frameworks.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: Open-Prompt-Injection 503 · MGDebugger 101 (synced Oct 4, 2026).

Common questions

What is the difference between Open-Prompt-Injection and MGDebugger?
Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. MGDebugger: Multi-Granularity LLM Debugger. See the comparison table for live GitHub stats and shared categories.
When should I choose Open-Prompt-Injection over MGDebugger?
Choose Open-Prompt-Injection over MGDebugger when Tags unique to Open-Prompt-Injection: llm-security, prompt-injection, security-and-privacy; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications; More GitHub stars (503 vs 101) - visibility, not fit.
When should I choose MGDebugger over Open-Prompt-Injection?
Choose MGDebugger over Open-Prompt-Injection when Pricing: MGDebugger is free to use under MIT license but may require users to manage model hosting costs and dependencies.; Requirements: Min 4 GB RAM; Requires Python version 3.8 or later; vLLM version 0.6.0 or later must be installed for model inference; Tags unique to MGDebugger: automatic-program-repair, code-generation, debugger, large-language-models; When you need to perform granular analysis on complex codes, progressing from subfunctions to the whole system to ensure precise error detection and correction.
When should I avoid Open-Prompt-Injection?
You require broader, more generalized security features not centered on prompt injection attacks. Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.
When should I avoid MGDebugger?
Avoid using MGDebugger if you operate primarily on Mac systems and do not require support for quantized models (as some essential dependencies are unsupported on MacOS). If your model does not align well with the DeepSeek-Coder-V2-Lite-Instruct or similar models, since the effectiveness of MGDebugger might vary without support for those particular frameworks.
Is Open-Prompt-Injection or MGDebugger more popular on GitHub?
Open-Prompt-Injection has more GitHub stars (503 vs 101). Stars measure visibility, not whether either tool fits your constraints.
Are Open-Prompt-Injection and MGDebugger open source?
Yes - both are open-source projects on GitHub (Open-Prompt-Injection: MIT, MGDebugger: MIT).
Where can I find alternatives to Open-Prompt-Injection or MGDebugger?
GraphCanon lists graph-backed alternatives at Open-Prompt-Injection alternatives and MGDebugger alternatives (Open-Prompt-Injection markdown twin, MGDebugger markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, Open-Prompt-Injection or MGDebugger?
Open-Prompt-Injection: Very active. MGDebugger: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for Open-Prompt-Injection and MGDebugger?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Open-Prompt-Injection trust report; MGDebugger trust report.

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