An embedding is a mathematical representation that converts discrete data—words, sentences, images, code—into dense numerical vectors in a continuous space. In this space, semantically similar items cluster together: the embeddings for "dog" and "puppy" are closer than "dog" and "computer." For Web3 AI systems, embeddings are fundamental to how models understand and retrieve information, creating both capabilities and attack surfaces.

How Embeddings Work

Raw data cannot be directly processed by neural networks—networks operate on numbers. Embeddings solve this by mapping each input element to a vector of floating-point numbers:

1"blockchain" → [0.23, -0.45, 0.12, ..., 0.78]  (hundreds of dimensions)
2"ethereum"   → [0.21, -0.48, 0.15, ..., 0.75]  (nearby in space)
3"banana"     → [-0.65, 0.32, -0.44, ..., 0.12] (far away)

These vectors capture semantic relationships learned during training. Words used in similar contexts develop similar embeddings.

Types of Embeddings

Word Embeddings: Individual words mapped to vectors. Classic approaches like Word2Vec and GloVe created fixed embeddings; modern systems use contextual embeddings that vary based on surrounding text.

Sentence/Document Embeddings: Entire passages compressed into single vectors, enabling semantic search and similarity comparison.

Code Embeddings: Source code represented as vectors, powering code search, clone detection, and vulnerability pattern matching.

Image Embeddings: Visual content encoded as vectors, enabling image search and multimodal AI systems.

Embeddings in Web3 AI

Embeddings power critical Web3 AI functionalities:

RAG Systems: Retrieval-augmented generation uses embeddings to find relevant documents. User queries are embedded and compared against document embeddings to retrieve context for the LLM.

Semantic Search: Finding similar smart contracts, transactions, or content based on meaning rather than keyword matching.

Code Analysis: Identifying vulnerable code patterns by comparing new code embeddings against known vulnerability embeddings.

Fraud Detection: Embedding transaction patterns to identify suspicious activity similar to known fraud.

Security Implications

Embeddings create specific security considerations:

Embedding Collision Attacks: Crafting malicious content with embeddings similar to benign content, causing it to be retrieved or classified incorrectly.

Semantic Confusion: Adversarial text that appears different to humans but has similar embeddings to target content, bypassing semantic filters.

RAG Poisoning: Injecting documents with embeddings designed to match common queries, ensuring malicious content is retrieved and influences model outputs.

Privacy Leakage: Embeddings may encode sensitive information from training data that can be partially recovered through analysis.

Model Inversion: Attempting to reconstruct original data from embeddings, potentially exposing private training information.

Embedding Similarity and Retrieval

Embedding-based retrieval uses distance metrics to find similar items:

Cosine Similarity: Measures angle between vectors, ignoring magnitude. Most common for text embeddings.

Euclidean Distance: Straight-line distance between vector endpoints.

Dot Product: Related to cosine similarity, often used for efficiency.

Retrieval systems return items with highest similarity to the query embedding. Attackers can exploit this by:

• Creating content that maximizes similarity to valuable queries
• Generating adversarial documents that bypass relevance filters
• Poisoning embedding models to distort similarity relationships

Testing Embedding Security

When auditing embedding-based systems:

Collision Testing: Generate content attempting to collide with target embeddings.

Boundary Exploration: Find inputs that create unexpected embedding positions.

Retrieval Manipulation: Test whether injected content can influence retrieval results.

Embedding Stability: Verify that minor input changes don't cause major embedding shifts.

Best Practices

For secure embedding usage:

• Validate retrieved content before using it in prompts
• Implement diversity in retrieval to avoid over-reliance on single results
• Monitor embedding distributions for anomalies suggesting manipulation
• Use multiple embedding models to reduce single-point-of-failure risks
• Regularly update embedding models as attacks evolve

Understanding embeddings is essential for building and auditing AI systems that rely on semantic similarity and retrieval operations.