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2 stories in the last 24h

Google’s new agent security system detects tool misuse, loops and rogue behavior

Google launched Agent Anomaly Detection in private preview, flagging agent tool misuse, prompt injection, privilege abuse, loops and rogue behavior in Security Command Center.

Agent Anomaly Detection is a reasoning-based oversight and audit layer for autonomous agents on Agent Runtime in the Gemini Enterprise Agent Platform, built with the Agent Development Kit (ADK) for Python (2.1.0 recommended), available in Private Preview. It detects selected OWASP agentic Top 10 risks including tool misuse, indirect prompt injection, identity and privilege abuse, agentic cascading failures, and rogue agents, plus operational risks like resource exhaustion. Analysis is layered: a statistical first pass over all traffic, an LLM-based reasoning layer for flagged sessions, and invocation-level analysis; findings publish to Security Command Center with severity, probability, rationale, and recommended actions.

Better Vector Search for Long Documents: Chunking Inside Manticore Searchnew

Manticore Search added automatic document chunking for vector columns, lifting long-document recall@5 from 55.1% to 83.3% in its benchmarks.

Manticore Search introduced a chunk_strategy option for model-backed vector columns in CREATE TABLE, offering five strategies (truncate, mean, fixed, recursive, sentence) with tunable max_tokens, overlap_tokens, and max_chunks, eliminating external splitters and separate chunk tables. On its 189-page, ~298k-word manual, sentence chunking improved recall@5 from 55.1% to 83.3% and MRR from 0.44 to 0.70, at roughly 2.5x RAM and 4x ingest time. Documents still return as single results; queries are never chunked.