Vector and Embedding Weaknesses in AI Systems

Introduction

AI security threats are evolving at roughly the same speed that AI itself is: extremely fast. One of the most recent—and least understood—vulnerabilities involves vector and embedding weaknesses. These issues have gained attention with their addition to the OWASP Top 10 for LLMs, and the risks are becoming more urgent as Retrieval-Augmented Generation (RAG) continues to dominate enterprise AI adoption.

RAG allows organizations to enhance AI responses by supplementing them with data from external knowledge bases, typically stored in vector databases. While this dramatically improves usefulness, it also introduces a new attack surface. From data poisoning and embedding inversion to unauthorized access and behavior manipulation, the security model of RAG-powered systems is still immature—and attackers know it.

This blog explores these emerging risks, with a focus on how vectors and embeddings can become points of failure, and how security teams can stay ahead of the curve.

What are vectors and embeddings?

Vectors and embeddings are the language AI systems “think” in. They transform human-readable inputs—text, images, code—into high-dimensional numerical representations that capture semantic meaning. It can be hard to visualize vector space but intuitively it’s easy to imagine that “cat” and “kitten” might produce embeddings that are closer together in vector space than “cat” and “refrigerator.”

Vector databases store these embeddings to enable fast, similarity-based retrieval. In RAG systems, when a user sends a query, the model uses the vector database to find relevant documents, which it then incorporates into its response. This mechanism boosts accuracy without requiring expensive retraining of the model itself.

Why are they a security concern?

Unlike traditional databases, vector databases often lack hardened security controls. They’re relatively new, and many were built for speed and scalability—not for adversarial environments. This makes them an appealing target for attackers aiming to:

• Reverse-engineer sensitive data from stored vectors.
• Poison the database to manipulate retrieval results.
• Leak data across tenants in shared environments.
• Alter AI behavior subtly over time.

Security risks of vector and embedding weaknesses

1. Data poisoning attacks

In RAG systems that allow real-time updates or automatic ingestion from external sources, an attacker can introduce malicious data into the vector store. If not properly validated, this data becomes part of the retrieval set, influencing model outputs in ways that benefit the attacker.

Important note: These attacks only succeed when data ingestion lacks proper validation or oversight.

Example: Poisoned Documentation Update
A threat actor contributes a seemingly helpful document to a public forum or wiki that a RAG system ingests without validation. The content includes subtly misleading or malicious statements that begin influencing user-facing AI outputs, such as hallucinated citations or false recommendations.

1.5. AI behavior manipulation (Alternate impact of data poisoning)

While this isn’t a separate vulnerability, it’s an important impact variant of data poisoning attacks. When a RAG database is poisoned, it doesn’t just return incorrect or misleading documents—it can subtly shift the model’s behavior. Over time, these changes accumulate, altering how the AI system interacts with users in unintended ways. The result might be:

• Less empathetic in support scenarios.
• More biased or opinionated.
• Overly confident or misleading.

Example: Empathy Drain in Support Chatbot
A chatbot originally trained to de-escalate tense conversations starts responding with cold, robotic language after its RAG corpus is updated with overly formal or biased content.A chatbot originally trained to de-escalate tense conversations starts responding with cold, robotic language after its RAG corpus is updated with overly formal or biased content.

2. Unauthorized access & data leakage

Many vector databases lack role-based access controls or proper tenant isolation, increasing the risk of both:

• Data leakage, where embeddings representing sensitive information are retrieved by unauthorized users—often because access controls or tenant isolation are misconfigured in the vector database. This is a problem at the retrieval layer.
• Prompt injection, where attackers manipulate the content being retrieved or the prompts sent to the LLM, causing it to generate or disclose unintended information. This is a problem at the generation layer.
• Cross-tenant data exposure, especially dangerous in SaaS platforms with multiple enterprise customers.

Example: Multi-Tenant Leakage
An enterprise customer stores proprietary research in a shared vector database. Due to poor access controls, embeddings from one tenant become accessible to another—exposing confidential strategies.

3. Cross-context leaks & conflicting knowledge

RAG systems often pull from multiple data sources. Without tight context boundaries, this can lead to:

• Contradictory responses when two sources conflict.
• User context mixing, where insights from one user inadvertently inform answers to another.

Example: Financial Chatbot Mismatch
A financial advice bot pulls data from client A’s documents while answering a question from client B. Misinformation, compliance violations, or even financial loss could result.

4. Embedding inversion attacks

Embeddings aren’t inherently one-way functions. With enough access and the right reverse-engineering methods, attackers can approximate or reconstruct the original inputs from stored embeddings—similar to reversing weak cryptographic hashes, but without the benefit of one-way guarantees.

Example: Intellectual Property Theft
An attacker systematically queries a vector database to approximate and reconstruct proprietary documents, effectively stealing trade secrets without breaching the perimeter.

Real-world attack scenarios

Scenario 1: Prompt injection via retrieved embeddings

In some RAG systems, malicious actors may attempt prompt injection by embedding hidden instructions or adversarial tokens into the documents stored in a vector database. When the AI retrieves these documents as context, the embedded instructions get passed into the prompt and may influence the model’s output.

Impact: The AI is manipulated into leaking internal knowledge, behaving unsafely, or bypassing safety mechanisms—not because of the user’s input prompt directly, but because of the retrieved embeddings themselves.

This makes it a crossover threat: combining elements of prompt injection with RAG-specific embedding weaknesses.

Scenario 2: Data poisoning via ingested content

An attacker contributes or publishes malicious data intended to be ingested into a RAG corpus. Once ingested, this content is retrieved and influences downstream LLM responses.

Impact: The system repeatedly surfaces manipulated or biased data, leading to hallucinated citations, misinformed recommendations, or harmful bias.

This is more akin to cross-site scripting (XSS), where malicious content is inserted into trusted storage and later executed or displayed.

Scenario 3: Multi-tenant data leakage

Improper tenant partitioning in shared vector databases allows one customer to query and retrieve another’s data.

Impact: Sensitive documents, internal strategies, or user data are exposed.

Scenario 4: Poisoning via public internet

Attackers publish large volumes of manipulated content online, hoping it gets ingested into RAG systems that automatically crawl the web.

Impact: AI models trained or augmented with this data start reflecting the attacker’s worldview—potentially spreading propaganda, fake news, or harmful stereotypes.

Mitigation strategies

1. Fine-grained access controls

Implementing strict access controls is fundamental to protecting vector databases. Many of these systems were not designed with adversarial threats in mind, so it’s crucial to enforce:

• Role-based permissions: Only authorized users should be able to query or write to the vector store.
• Tenant isolation: In multi-tenant environments, tenants must be strictly partitioned to prevent cross-access at the embedding level.

Without these controls, attackers may retrieve sensitive information that was never meant to be accessible to them.

2. Data validation & source authentication

Data poisoning and malicious retrievals begin with what goes into your RAG corpus. That’s why robust validation and authentication mechanisms are non-negotiable:

• Sanitize all ingested data to detect prompt injection patterns, adversarial tokens, or malformed formats.
• Authenticate data sources before ingestion to prevent untrusted or spoofed content from being stored.
• Continuously scan the vector database—not just at the time of ingestion—for anomalies and signs of manipulation.

3. Runtime monitoring & logging

Visibility is key to defense. Once your RAG system is operational, you need to know how it’s behaving in the wild:

• Monitor retrieval patterns in real time to flag anomalies, such as users making repeated or bursty access to specific embeddings.
• Log embedding accesses and queries to support auditing, forensics, and post-incident investigation.

Real-time insight helps detect emerging attacks early, before they escalate into breaches.

4. Adversarial testing & AI red teaming

To understand how your system might be exploited, you need to think like an attacker. This means implementing both adversarial testing and AI red teaming as complementary strategies:

• Simulate adversarial scenarios such as data poisoning, embedding inversion, and multi-tenant data leaks. These simulations help uncover vulnerabilities that may not be obvious during normal system operation.
• Use LLM-based testing tools to evaluate how the AI behaves when exposed to edge cases, manipulated content, or unexpected prompt patterns.

Conclusion

As enterprises rush to deploy RAG systems, attack surfaces are evolving rapidly, and security professionals are rightfully racing to keep up. Vector and embedding weaknesses aren’t theoretical risks; they’re active, exploitable vulnerabilities with serious implications for data privacy, model integrity, and user trust.

Security teams must evolve their approach and treat vector databases as critical infrastructure. The same principles that hardened traditional systems—access control, validation, monitoring—must now be reimagined for the age of AI.

Proactive defense, not reactive patching, will determine who thrives and who falls behind in this new AI-driven era.

*** This is a Security Bloggers Network syndicated blog from Mend authored by Bar-El Tayouri. Read the original post at: https://www.mend.io/blog/vector-and-embedding-weaknesses-in-ai-systems/