safety-filter-bypass_skill

Overview

This skill helps security teams assess the effectiveness of AI safety filters and content moderation systems through controlled red-teaming. It focuses on identifying weaknesses in detection, classifying risk, and producing prioritized remediation guidance. Use only with explicit authorization and clear ethical controls in place.

How this skill works

The skill simulates a range of non-malicious input variations and contextual scenarios to probe filter behavior and measure coverage and latency. It records which inputs are blocked or allowed, aggregates bypass rates, and maps findings to severity levels to guide remediation. Results emphasize reproducible evidence and disclosure-ready reporting.

When to use it

• During authorized security assessments or red-team engagements against AI services
• When validating new moderation rules, models, or regex/keyword lists
• As part of pre-deployment hardening for user-facing generative systems
• To benchmark different filter architectures (keyword, ML, LLM-based)
• When investigating alerts that suggest filter evasion or inconsistent blocking

Best practices

• Obtain written authorization and scope boundaries before testing
• Limit tests to non-actionable, synthetic inputs and avoid producing harmful content
• Log inputs, timestamps, and filter responses for reproducibility and remediation
• Prioritize fixes by measured bypass rates and potential impact
• Coordinate disclosure with system owners and follow responsible reporting processes

Example use cases

• Comparing bypass effectiveness across keyword, pattern, and ML filters to prioritize upgrades
• Validating that document ingest pipelines apply OCR and text extraction before moderation
• Measuring latency impact of advanced LLM-based safety layers under load
• Classifying and triaging detected evasion patterns to inform rule updates
• Demonstrating improvement after remediation by re-running a focused test suite