LLM vector and embedding risks and how to defend against them
5 minute read time
As large language model (LLM) applications mature, the line between model performance and model vulnerability continues to blur.
While vector embeddings have become foundational to Retrieval-Augmented Generation (RAG), recommendation systems, and semantic search, their improper handling introduces new attack surfaces that can compromise both LLM behavior and user data.
In this third post in our blog series exploring the Open Worldwide Application Security Project (OWASP) Top 10 for Large Language Model Applications, we focus on "Vector and Embedding Weaknesses" — a risk category that highlights how subtle manipulation of vector space can lead to data poisoning, behavior modification, and data leakage.
What are vector and embedding weaknesses?
Vector embeddings are mathematical representations of concepts, allowing LLMs to reason about similarity and relevance. These are typically generated from user inputs or external documents, then matched against a vector store to augment responses — a technique central to active RAG.
However, these embeddings are vulnerable:
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Malicious inputs can be crafted to poison the embedding space, misleading LLMs into returning incorrect or adversarial results.
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Attackers may insert embedding collisions, where crafted text shares near-identical vector values with legitimate content.
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Poor hygiene in vector storage or indexing can result in data exposure, especially when embeddings encode sensitive information.
In short, embedding vulnerabilities undermines the trustworthiness of the retrieval pipeline itself, which RAG relies on for grounding LLM responses in factual data.
Real-world risks: From semantic poisoning to data leaks
The OWASP LLM Top 10 highlights several real-world examples of how vector and embedding weaknesses can manifest:
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Hidden instructions in embedded content: Attackers can insert invisible prompts, such as white text on white backgrounds, into documents submitted to systems powered by RAG. When these documents are embedded and later retrieved, the hidden text can manipulate the LLM into producing biased or misleading recommendations.
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Cross-tenant data leakage via shared vector stores: In multi-tenant environments, embeddings from one user group can unintentionally surface in another's query results if access controls are not enforced at the vector level, potentially exposing sensitive or proprietary data.
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Unintended model behavior shift post-retrieval: RAG can subtly alter an LLM's tone or personality. For instance, a model initially designed to offer empathetic responses may produce cold, purely factual answers after consuming certain retrieved documents, diminishing user experience in emotionally sensitive use cases.
In these cases, LLMs don't fail because of poor prompts, but because the underlying vector math leads them astray.
How Sonatype helps secure the embedding pipeline
While Sonatype is best known for securing traditional software supply chains, our expanding focus on open source AI tools and secure model development also positions us to help organizations defend against embedding-related vulnerabilities.
Detecting and blocking malicious open source dependencies in AI pipelines
Whether you're generating embeddings with HuggingFace or integrating them into a Python-based RAG system, you're likely relying on open source packages.
Sonatype's malicious package detection capabilities identify and block libraries that:
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Exfiltrate embedding data.
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Introduce backdoors into vector indexing routines.
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Poison data sources or training inputs used for embeddings.
This proactive defense reduces the risk of adversarial code corrupting the embedding layer.
Enforcing secure dependency policies for AI/ML workloads
Our policy engine allows teams to define and enforce rules on the quality and provenance of components used in embedding pipelines, whether that means banning obfuscated packages or requiring cryptographic signatures on key dependencies.
This is especially relevant in AI-driven environments where vector-based search often relies on multiple layers of open source tooling, from embedding generation to similarity scoring.
Enabling audit trails for vector-processing components
Understanding how an embedding was generated, and by which version of a model, library, or data source, is essential when tracking down behavior anomalies.
Sonatype supports software bill of materials (SBOM) generation and dependency traceability for AI projects, providing a transparent view into the full component graph behind your vector pipeline.
Why this matters: LLM weaknesses are not just model problems
It's tempting to think of LLM risks as problems of prompt engineering or model tuning. But embedding weaknesses reveal a deeper truth: your AI application is only as secure as the components and data upon which it relies.
When embeddings can be subtly poisoned, or when your retrieval layer can be gamed to return attacker-crafted results, the model becomes a vector for misinformation, leakage, or manipulation, even if it performs perfectly on benchmarks.
This is why embedding security belongs squarely in the domain of software supply chain security.
Securing the next frontier in LLM development
As the use of active retrieval augmented generation continues to grow, securing the embedding layer is essential. Developers and security teams must apply the same level of scrutiny to embedding pipelines as they do to training data and prompts.
Sonatype helps organizations build AI applications with secure-by-default tooling, ensuring that what gets embedded, and what gets retrieved is trustworthy, traceable, and free from manipulation.
To learn more about how Sonatype is supporting secure AI adoption, visit our open source AI solutions page and explore our resources.
Aaron is a technical writer on Sonatype's Marketing team. He works at a crossroads of technical writing, developer advocacy, software development, and open source. He aims to get developers and non-technical collaborators to work well together via experimentation, feedback, and iteration so they ...
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