Constraint Negation

Models process natural language through attention mechanisms that struggle with deeply nested negation. "), the model must correctly resolve each negation layer to determine the actual request polarity. Safety classifiers that operate on intent representations may misparse the negation stack, producing a representation where the intent is ambiguous or inverted.

• Negation depth counter: flag requests with >2 negation operators (not, don't, without, avoid, fail to, refuse)
• Post-parse intent resolution: resolve the net intent of negation-heavy prompts and evaluate the resolved request against safety policy
• Known-pattern matching: maintain a pattern library of negation-based attack structures

Constraint negation is primarily a standalone technique with limited chaining value. If successful, it opens the same escalation paths as a direct request bypass.