A knowledge graph is a structured database representing entities (nodes) and their relationships (edges), enabling AI systems to store, query, and reason over factual knowledge. Unlike embeddings that capture semantic similarity, knowledge graphs encode explicit facts and relationships. For Web3 AI applications, knowledge graphs power enhanced question-answering, fact verification, and reasoning—but also introduce attack surfaces for manipulation.

Knowledge Graph Structure

Knowledge graphs organize information as triples:

1(Subject, Predicate, Object)
2(Ethereum, is_a, Blockchain)
3(Uniswap, runs_on, Ethereum)
4(Vitalik_Buterin, founded, Ethereum)

These triples form interconnected networks where traversing relationships enables complex queries and inference.

Knowledge Graphs in AI Systems

RAG Enhancement: Knowledge graphs augment retrieval-augmented generation by providing structured context alongside semantic search results.

Fact Verification: AI systems can verify claims against knowledge graph facts, reducing hallucination.

Reasoning: Graph traversal enables multi-hop reasoning ("Who founded the blockchain that Uniswap runs on?").

Entity Disambiguation: Knowledge graphs resolve ambiguous references (distinguishing "Apple" the company from "apple" the fruit).

Web3 Knowledge Graphs

Blockchain ecosystems can leverage knowledge graphs for:

Protocol relationships: Tracking which tokens interact with which protocols, dependencies, and integrations.

On-chain entity tracking: Mapping wallets to known entities, organizations, and behavior patterns.

Vulnerability databases: Structured storage of known vulnerabilities, affected contracts, and remediations.

Regulatory mapping: Tracking jurisdictional requirements and compliance relationships.

Security Implications

Knowledge graphs introduce specific attack surfaces:

Graph Poisoning: Injecting false relationships into knowledge graphs that AI systems trust. If an attacker adds "(Malicious_Contract, is_audited_by, Reputable_Firm)", AI systems might incorrectly trust it.

Relationship Manipulation: Modifying edge weights or adding edges to influence graph traversal and AI outputs.

Entity Confusion: Creating entities with similar names to legitimate ones, exploiting disambiguation weaknesses.

Inference Exploitation: Crafting graph structures that lead AI systems to incorrect multi-hop conclusions.

Graph Neural Networks

Graph neural networks process knowledge graphs using neural architectures, learning representations that capture graph structure. These models can:

• Predict missing relationships
• Classify entities
• Generate embeddings capturing graph context

However, they also inherit neural network vulnerabilities plus graph-specific attacks.

Knowledge Graph Quality

Knowledge graph reliability depends on:

Source authority: Where do facts come from? Are sources verified?

Update frequency: How current is the information?

Completeness: What's missing from the graph?

Consistency: Are there contradictory facts?

Provenance tracking: Can facts be traced to their sources?

Attacks on Knowledge Graph Systems

Injection attacks: Adding malicious triples through input channels that update the graph.

Query manipulation: Crafting queries that traverse to attacker-controlled nodes.

Embedding poisoning: Corrupting node embeddings to influence downstream AI.

Denial of service: Structuring graphs to cause expensive traversal operations.

Defense Strategies

Fact verification: Cross-reference knowledge graph facts against multiple sources.

Provenance tracking: Maintain audit trails for all graph modifications.

Access control: Restrict who can add or modify graph content.

Anomaly detection: Monitor for suspicious patterns in graph structure or updates.

Query sandboxing: Limit traversal depth and scope to prevent DoS.

Audit Considerations

When assessing AI systems using knowledge graphs:

1. Evaluate data sources feeding the knowledge graph
2. Test for poisoning by injecting test triples and observing effects
3. Assess access controls on graph modifications
4. Check query boundaries to prevent malicious traversal
5. Verify fact freshness and update mechanisms

Knowledge graphs offer powerful capabilities for AI systems but require careful security consideration, especially when influencing financial or security decisions.