Trust Graph

Every agent node must be bound to a Decentralized Identifier (DID) to prevent Sybil attacks. Without a globally unique, self-sovereign identity, a single adversary can spawn thousands of fake agents to dominate reputation scores.

• Each agent's DID is cryptographically generated and resolvable without a central registry
• Verifiable Credentials attest to agent attributes and authorizations
• Prevents identity forgery that undermines transitive trust calculations

Trust must attenuate as it propagates through the graph. If Agent A trusts Agent B, and B trusts Agent C, A's derived trust in C must be strictly less than its direct trust in B.

• Implements a decay factor (typically 0.5–0.8) per hop
• Prevents trust concentration where a single compromised node grants infinite reach
• Graph traversal algorithms enforce maximum path lengths to bound computational cost

Trust is not static—it must be continuously recalculated based on observed behavior versus expected behavior. An agent that deviates from its historical interaction patterns triggers a trust downgrade.

• Granger Causality tests detect if one agent's actions predict another's, flagging potential covert coordination
• Graph Neural Network anomaly detection identifies nodes with statistically aberrant edge weights
• Temporal analysis prevents slow-boil attacks that gradually erode trust thresholds

Every trust edge must be backed by a cryptographic attestation from the endorsing agent. This creates an immutable, auditable trail of why trust was granted.

• Threshold Signatures require multiple agents to collaboratively endorse a new node, raising the bar for collusion
• Endorsements include metadata: timestamp, context, and confidence level
• Enables post-hoc forensic analysis when a trusted agent is later identified as malicious

The trust graph must remain consistent even when a subset of agents behaves arbitrarily or maliciously. Byzantine Fault Tolerance (BFT) consensus mechanisms ensure that trust state updates are agreed upon by honest nodes.

• Requires 2f+1 honest agents to tolerate f Byzantine faults
• Prevents a malicious minority from rewriting the trust graph state
• Essential for decentralized systems without a central trust authority

Agents can prove they meet a trust threshold without revealing their full interaction history. Zero-Knowledge Proofs (ZKPs) enable privacy-preserving trust verification.

• An agent proves 'my reputation score exceeds 0.85' without disclosing which peers endorsed it
• Prevents graph enumeration attacks where adversaries map the entire trust topology
• Critical for competitive or sensitive multi-agent deployments

The application of GNNs to learn the normal interaction patterns in an agent network topology and identify anomalous nodes or edges. This is the primary computational engine for dynamic trust scoring in large-scale Trust Graphs. By analyzing message-passing patterns, GNNs detect subtle indicators of collusion, such as unexpected clustering coefficients or abnormal edge weight distributions.

The property of a distributed system to reach consensus even when an arbitrary number of nodes act maliciously. Trust Graphs provide the reputation-weighted quorum necessary for practical BFT in agent systems. Instead of treating all votes equally, a BFT protocol informed by a Trust Graph can weight votes by trust scores, making Sybil attacks economically and computationally infeasible.

A security mechanism where an agent generates irrefutable cryptographic proof of its current software stack and identity. This proof serves as a genesis trust signal for a Trust Graph. Before any historical interaction data exists, a successful remote attestation—often backed by a Trusted Execution Environment (TEE)—provides a hardware-rooted bootstrap of trust for a newly introduced agent.

An indirect coordination mechanism where agents modify their shared environment to trigger actions from others. Trust Graphs must distinguish between legitimate stigmergy and covert channels. While stigmergic signals (like updating a shared knowledge base) are overt and beneficial, malicious agents can exploit environmental modifications to encode hidden messages that bypass formal communication monitoring.