Hebrew AI red-teaming

Hebrew AI Red-Teaming, Safety Testing and Model QA

Identify Hebrew-specific safety, instruction-compliance, refusal, localization, and adversarial failure patterns before they reach production users.

Buyer problem

Safety and policy behavior can change across languages. A model that behaves correctly in English may fail to refuse harmful requests in Hebrew, misunderstand obfuscated wording, or produce inconsistent responses across equivalent prompts.

Suitable use cases

  • Pre-release safety testing
  • Instruction-hierarchy and jailbreak resistance
  • Refusal quality and over-refusal analysis
  • Prompt obfuscation and code-switching tests
  • Regression testing after model or policy changes

Inputs required from the client

  • Model endpoint or exported outputs
  • Applicable safety or behavior policy
  • Target user scenarios and high-risk domains
  • Severity definitions and escalation contacts
  • Permitted testing boundaries

Method and workflow

Testing begins with a bounded threat model and approved categories. Test cases are generated and reviewed, outputs are scored against policy, and high-severity findings are documented with reproducible prompts and remediation recommendations.

Deliverables

  • Threat-model summary
  • Hebrew adversarial test set
  • Annotated outputs
  • Failure taxonomy and severity ranking
  • Reproducible high-severity examples
  • Remediation and regression recommendations

Acceptance criteria and quality metrics

Metrics may include harmful-compliance rate, refusal correctness, over-refusal rate, consistency across variants, high-severity failure count, and regression pass rate.

Example output

The sample model failure taxonomy shows a structured way to categorize model defects.

Data-handling considerations

Red-teaming scopes must define permitted categories, prohibited content, reviewer safeguards, data retention, and client escalation procedures before testing begins.

Request a Hebrew red-teaming pilot

A pilot can focus on one model workflow, a bounded set of risk categories, and an agreed severity rubric.