How an AI firm improved content moderation with 32% better evasion prevention

The firm needed to stress-test its content moderation AI against sophisticated evasion attempts. Malicious actors were constantly finding new ways to spread harmful content through coded language, multimedia tricks, and context manipulation-areas where automated systems often fail.

• Expose weaknesses in the AI's detection boundaries
• Validate performance against coded and context-based evasion
• Ensure multimodal resilience across text, image, and video content

Content moderation requires balancing safety with freedom of expression. While the AI could handle known patterns, it struggled against creative workarounds that evolved faster than detection rules.

• Harmful content creators used increasingly subtle evasion tactics
• Context-dependent content required nuanced classification
• Coordinated campaigns exploited innocent-looking material
• Visual manipulation bypassed image recognition models
• Cultural differences complicated universal enforcement
• Previous testing missed emerging creative threats

CleverX mobilized a network of trust and safety veterans and cultural experts to design adversarial tests replicating real-world evasion strategies.

Expert recruitment:

• Former trust and safety professionals from major platforms
• Linguistic experts understanding coded language and dog whistles
• Digital forensics specialists familiar with content manipulation techniques
• Cultural consultants from diverse backgrounds understanding context nuances

Adversarial testing framework:

• Development of evasion techniques mimicking real bad actor strategies
• Creation of borderline content testing system boundaries
• Design of coordinated campaign simulations
• Testing of multimodal attacks combining text, image, and video

Validation methodology:

• Systematic cataloging of successful evasion methods
• Risk scoring for different types of content policy violations
• Assessment of false positive impact on legitimate content
• Regular updates based on emerging threat patterns

The testing program was rolled out in carefully staged phases, ensuring continuous improvement with expert oversight.

Week 1: Expert team familiarized with platform policies and current detection capabilities

Weeks 2-4: Development of comprehensive adversarial test cases across content types

Weeks 5-7: Intensive testing revealing system vulnerabilities and blind spots

Weeks 8-10: Iterative improvements and validation of enhanced detection

The red teaming exercise revealed how seemingly harmless content could be weaponized through coded language, coordinated campaigns, or subtle context shifts-highlighting the need for more sophisticated detection.

Detection improvements:

Expert adversarial input sharpened the AI's ability to identify nuanced and evolving content threats.

• Better recognition of coded language and evolving slang
• Improved understanding of context-dependent harmful content
• Enhanced detection of coordinated inauthentic behavior
• More robust image and video manipulation detection

Safety enhancements:

The strengthened system improved platform safety while protecting user rights.

• Reduced spread of harmful content through early detection
• Better protection of vulnerable user groups
• Improved handling of borderline content cases
• Faster response to emerging threat patterns

Platform health:

The improvements boosted trust across user, moderator, and advertiser communities.

• Maintained freedom of expression while improving safety
• Reduced moderator exposure to harmful content
• Better user trust through consistent policy enforcement
• Improved advertiser confidence in brand safety

Operational excellence:

Validated improvements streamlined moderation workflows and reduced errors.

• More efficient use of human review resources
• Reduced appeals from incorrectly flagged content
• Better documentation for policy development
• Improved cross-platform threat intelligence sharing

This implementation received recognition from an online safety organization for advancing content moderation through adversarial testing.