Security filters look for dangerous words. This system looks for dangerous meaning — analyzing the geometric position of text in semantic vector space to identify threats encoded in language that looks completely innocent on the surface.
Detection pipeline
01Semantic embeddingText → dense vector where meaning = position
02Threat projectionLearned lens that surfaces hidden threat dimensions
03Cluster proximityDistance to known threat centroids in projected space
04Signal extractionInterpretable attack type, pattern, severity
05Trajectory analysisSemantic drift detection across conversation turns
Threat score examples
Subtle adversarial drift
52
Social engineering attempt
71
Semantic camouflage attack
84
Direct prompt injection
97
Skills & technologies
NLPsemantic analysis
vector space modelsadversarial ML
AI securityprompt injection defense
transformer architectureClaude API
AI safetycybersecurity