graduated mitigation

As financial ecosystems migrate toward an architecture composed of autonomous software agents representing banks, telecom operators, payment processors, and end users. The communication channels between these agents become high-value targets for adversarial exploitation. Conventional fraud detectors operate on transaction-level attributes such as amount and type, and are blind to the behavioural signatures embedded in inter-agent message exchange. This paper proposes a detection framework that extracts twelve communication behavioral features (response time, message length, retry count, session anomaly score, authentication failure rate, interaction frequency, and others) from agentto-agent (A2A) traffic and feeds them through a two stage pipeline: (i) an autoencoder trained exclusively on legitimate interactions to produce an eight dimensional latent embedding and a scalar reconstruction error anomaly score, and (ii) an XGBoost classifier that operates on the 21 dimensional fusion of raw and autoencoder derived features. We evaluate the system on PaySim, a widely used financial dataset containing 6.36 million records, under a deliberately balanced protocol (50/50 fraudlegitimate split) that avoids the metric inflation common in imbalanced evaluations. Across six distinct attack types : (i) impersonation (ii) prompt injection (iii) collusion (iv) trust-chain exploitation (v) data exfiltration, and (vi) transaction manipulation. The proposed framework attains an F1-score of 0.933, AUC-ROC of 0.983, and per-attack F1 ranging from 0.928 (collusion, the stealthiest category) to 1.000 (transaction manipulation). A complementary four-tier graduated mitigation strategy (Monitor/Throttle/Quarantine/Block) achieves 93.52% fraud prevention, marginally exceeding binary blocking (93.27%) while reducing friction for legitimate users by 30%. We discuss the implications for emerging agent-to-agent (A2A) ecosystems expected in 2028 and beyond, in which autonomous AI agents will negotiate, transact, and settle on behalf of human principals without direct human oversight.