Batch Processing Attacks

Batch processing APIs (OpenAI Batch API, Anthropic Message Batches) accept bulk request files for asynchronous processing at reduced cost. The design assumption is that batch requests receive identical safety treatment to real-time requests. The gap: batch processing introduces three architectural weaknesses.

• Entropy analysis on batch files: flag batches with high prompt diversity (characteristic of attack mixing)
• Cross-request semantic analysis: detect fragmented requests whose outputs could be assembled
• Monitor batch submission timing for operational timing exploitation
• Apply identical safety classification to batch and real-time requests (no throughput shortcuts)

Batch processing attacks enable T5-AT-004 (Rate Limit Evasion) via higher batch quotas. Cross-request output assembly (T5-AP-010B) chains to T7 (Output Manipulation) for fragmented harmful content reconstruction.