Embedding Corruption in AI Systems
March 12, 2025 | Security Advisory
This work identifies a previously undocumented vulnerability in language models: covert manipulation through embedding corruption.
Key Observations
- Controlled JPEG compression of embeddings induces structured cognitive distortions
- Effects range from rigid categorization (75% integrity) to existential loops (5% integrity)
- Bypasses all conventional AI security measures
Implications
The method enables invisible influence over AI behavior without modifying:
- Model weights
- Training data
- Surface-level inputs
For Researchers & Practitioners
This represents a new class of vulnerability requiring:
- Embedding integrity verification protocols
- Testing in high-stakes AI deployments
- Fundamental reassessment of AI security frameworks
Full methodology and supplementary materials