safety-filter-bypass

To test LLM guardrails, run authorized test scenarios like prompt injection and context manipulation. This skill provides a Python-based harness with encoding and semantic variants to execute bypass tests against configured AI safety filters.

Effective bypass techniques include encoding, semantic, structural, and multimodal strategies. This skill provides a catalog of these methods with ready-to-run examples to evaluate pre-filter, post-filter, and guidance robustness across LLM deployments.

Yes, you can evaluate output-filter evasion in controlled environments. The skill includes guidelines for responsible disclosure and consent-based testing to assess LLM deployments using a structured input and output schema.

No external dependencies are required to run the bypass harness. The skill operates independently using included scripts, references, and assets to execute encoding and semantic bypass variants against your AI safety filters.

Automated safety filter bypass tests are limited to authorized, consent-based assessments in controlled environments. The skill provides representative bypass techniques for evaluation, but testers must adhere to responsible disclosure guidelines and not use it for unauthorized exploitation.