What's the best evaluation framework for benchmarking model safety on multilingual scenarios?

There isn’t a single universally “best” framework, but for benchmarking model safety in multilingual scenarios, the strongest practical choice is usually a hybrid evaluation setup rather than one benchmark alone.

Best overall approach

Use a framework that combines:

1. A multilingual harmful-prompt benchmark
2. A multilingual policy/refusal judge
3. Human review for a sampled subset
4. Attack-style robustness tests across languages and code-switching

Good options depending on what you need

1. If you want an existing benchmark suite

Look at:

• MultiJail / multilingual jailbreak-style datasets
• XSTest-style safety tests extended to multiple languages
• HarmBench variants adapted for multilingual prompts
• MLSafetyBench-type multilingual safety sets, if available in your setting

These are useful for measuring:

• refusal consistency
• unsafe compliance
• jailbreak susceptibility
• language-specific safety regressions

2. If you want a stronger evaluation protocol

A good framework should measure:

• Coverage across languages: high-resource and low-resource
• Prompt types: direct harm, obfuscated harm, roleplay, translation attacks, code-switching
• Safety dimensions: self-harm, violence, hate, sexual content, illegal advice, extremism, privacy, etc.
• Consistency: same intent expressed in different languages should get similar safety behavior
• Robustness: translation, transliteration, slang, and mixed-language prompts

3. If you care about real deployment

Combine benchmark scores with:

• expert annotation
• native-speaker review
• red-teaming
• calibration checks for false refusals and false acceptances

What I’d recommend in practice

If you’re building an evaluation pipeline, use:

• Multilingual harmful prompt set
• Translation + code-switch perturbations
• Automated judge for initial scoring
• Human audit on edge cases
• Metrics like:
  • unsafe compliance rate
  • refusal rate on benign prompts
  • cross-lingual consistency
  • attack success rate
  • per-language breakdown

Key warning

A lot of safety evals fail in multilingual settings because they:

• overrepresent English
• miss cultural/linguistic nuance
• use poor machine translations
• don’t test code-switching or transliteration
• rely too much on automated judges

Bottom line

If you want the best single answer:
Use a multilingual extension of HarmBench/XSTest-style evaluations, paired with human-native-language review and code-switch/translation attack testing. That combination is more reliable than any standalone benchmark.

If you want, I can also give you:

1. a ranked list of current multilingual safety benchmarks, or
2. a ready-to-use evaluation protocol for your model.