Recovering Input Text from Hidden States: Study of Gradient-Based Inversion of Decoder-Only Language Models
arXiv:2607.00852v1 Announce Type: new Abstract: This work studies the hidden-state inversion problem: recovering the original input token sequence of a decoder-only language model from its last-layer hidden states. Rather than treating inversion as a one-shot reconstruction, we study it as a continuous embedding-space optimisation in which a soft proxy is driven towards the leaked target without any hard-token projection during the search, and a token is committed only once, at the end of the inner loop. This design choice has two consequences which are the main focus of this paper. First, kee
The increasing sophistication and widespread deployment of large language models necessitate deeper understanding and control over their internal states for security and privacy. This research emerges as LLM capabilities advance, making their hidden states richer in information.
This research highlights a significant privacy and security vulnerability in decoder-only language models, as sensitive input information can potentially be recovered from their internal states. It underscores the critical need for robust privacy-preserving mechanisms in AI systems.
The ability to invert hidden states means that model outputs or even access to internal states could inadvertently leak training data or user input, necessitating new design principles for privacy and security in LLMs.
- · AI security researchers
- · Privacy-enhancing technology developers
- · Regulatory bodies focused on data privacy
- · Developers of proprietary LLMs with poor security
- · Users relying on LLMs for sensitive data processing
- · Organizations without robust data anonymization practices
Increased focus on differential privacy and secure multi-party computation within LLM architectures.
Development of new attack vectors that exploit hidden state leakage to extract sensitive information or intellectual property.
Potential for new regulations mandating 'hidden state transparency' or strong inversion resistance for AI models in sensitive applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL