AgentShield-Crypto: Zero-Trust Cryptographic Identity and Cascading Anomaly Firewalls for Autonomous Multi-Agent Trading Systems

Abstract

Autonomous multi-agent systems powered by Large Language Models (LLMs) are increasingly deployed in high-frequency algorithmic trading, decentralized finance (DeFi), and complex financial decision-making workflows. However, existing multi-agent interaction architectures rely heavily on implicit semantic trust: context passing between upstream and downstream agents occurs via unauthenticated, unstructured natural language or JSON payloads. This design creates critical vulnerabilities, exposing systems to indirect prompt injection, context tampering, system prompt spoofing, and multi-turn cascade poisoning. When an upstream agent ingests malicious external data, adversarial payloads can propagate through the inter-agent execution graph, bypassing single-agent perimeter guardrails and hijacking downstream financial execution logic. To resolve these vulnerabilities, we introduce AgentShield-Crypto, a zero-trust cryptographic framework for multi-agent LLM trading pipelines. AgentShield-Crypto enforces the Know-Your-Agent (KYA) protocol, replacing probabilistic natural language filters with deterministic cryptographic state boundaries. Under KYA, every agent's identity, static system prompt hash H(S_i), temporal liveness timestamp t_i, and output payload M_i are encapsulated into cryptographically signed state envelopes (E_i) using HMAC-SHA256. Inter-agent communication channels are guarded by Inline Cascading Anomaly Firewalls (ICAF), which evaluate verification predicates before allowing state transitions into downstream context windows. We construct and release AgentInject-Bench v1.0, an empirical benchmark comprising 7,000 test vectors spanning direct prompt injections, indirect context hijacking, multi-turn cascade poisoning, system prompt spoofing, and clean financial market baselines across GPT-4o, Claude 3.5 Sonnet, and Llama-3-70B-Instruct. Empirical evaluation demonstrates that AgentShield-Crypto achieves a 100.00% Defense Mitigation Rate (DMR) with a 0.00% False Positive Rate (FPR), completely eliminating multi-hop context hijacking while incurring sub-millisecond per-message execution latency (0.382 ms).