The Myth of “Safe” Embeddings
Vector embeddings are mathematical representations of data (such as text, images, or audio) that enable machines to assess similarity. For example, they power semantic search and related-document retrieval.
In the rush to build with LLMs, many organizations have adopted vector databases and embedding pipelines under the assumption that embeddings are “abstract math” - a scrambled representation that can’t be traced back to the original data. This assumption is dangerously wrong.
Embeddings are not one-way hashes. Unlike cryptographic functions, embeddings are vulnerable to inversion attacks, where attackers reconstruct original content from vectors. Treating them as inherently safe is like storing valuables behind frosted glass: blurred, but not secure.
What Is Embedding Inversion?
Embedding inversion is the process of recovering original inputs - like text or images - from their vector representations. Think of it as "unhashing" data you assumed was anonymized.
This threat is real and demonstrated in research:
Song & Raghunathan (2020) – Information Leakage in Embedding Models
Showed that sentence embeddings leak enough signal to recover significant portions of original text, proving embeddings are far from anonymized.
Huang et al. (ACL 2024) – Transferable Embedding Inversion Attack
Demonstrated that attackers can reconstruct text using surrogate models, even when they don’t have access to the target embedding model - making the attack surface broader than many assume.
Wang et al. (2025) – Diffusion-Driven Universal Model Inversion Attack for Face Recognition
Introduced a universal, training-free inversion method using diffusion models to reconstruct realistic face images from embeddings across recognition systems.
At the Confidential Computing Summit in June 2025, Cyborg demonstrated exactly this risk against ChromaDB showing how embeddings can be reversed. You can watch the demo here.
Why Security Leaders Should Care
For CISOs and ML engineers, the implications are clear: treat embeddings with the same protective rigor as raw data.
- Embeddings are often centralized in vector databases or shared across pipelines.
- They frequently lack robust encryption-in-use or strict access controls.
- If leaked, embeddings can be inverted - exposing sensitive information with no way to revoke it.
This is not theoretical. While risk levels may vary depending on model type, dimensionality, or context, the key takeaway is consistent: embeddings are not anonymized outputs - they are sensitive assets.
Tangible Examples of Exposure
- Healthcare: Imagine storing embeddings of medical records under the assumption that they are anonymized. Inversion attacks could reconstruct patient histories or PII - triggering HIPAA violations and eroding patient trust.
- Finance: Embeddings of transactions could be inverted to recover credit card details, account numbers, or behavioral patterns that enable fraud.
- Corporate IP: Internal chat logs or contract documents fed into embeddings could be reconstructed, leaking strategy, M&A details, or R&D secrets.
In each case, the outcome is the same: irreversible data exposure.
Defending Against Inversion
Security controls must evolve alongside AI implementations. Consider these best practices:
- Encryption Everywhere: At rest, in transit, and critically, in use - so embeddings never exist in plaintext.
- Key Ownership: Use BYOK (Bring Your Own Key) or HYOK (Hold Your Own Key) to retain control over decryption.
- Access Controls & Auditing: Enforce least privilege and maintain logs for every embedding access.
- Architectural Guardrails: Limit where and how embeddings are exposed - even internally.
While the severity of inversion depends on model architecture, domain, and vector dimensionality, the common thread across studies is that embeddings systematically leak more than most organizations assume.
The Path Forward
At Cyborg, we’ve been sounding the alarm because we believe securing the AI stack requires more than bolting on controls after the fact. Vector embeddings, in particular, must be handled with the same rigor as source data.
That’s why we designed CyborgDB - a database built with encryption, access controls, and secure query interfaces that protect against the risks of inversion from the ground up.
If you’re a security leader building with LLMs, now is the time to audit your embedding pipelines. Ask the hard questions:
- Are embeddings being stored securely?
- Who has access to them?
- What happens if they leak?
Embedding inversion is not science fiction. It’s here, it’s practical, and it’s a liability you can’t ignore. The good news is: there’s a path forward.
