Internship Research / Limiting Information Leakage in LLM-Based Systems for Network Applications

Inria Palaiseau; Palaiseau; On-site; EUR 12,000 - 17,000; Full time

Job summary

Inria Palaiseau is offering an internship focused on privacy-preserving techniques for LLM-based systems handling user requests. You will study information flows, formalize leakage metrics, and explore mechanisms to reduce leakage while preserving output quality.

Aimed at candidates with strong Python skills and an interest in privacy, security, and networking. The project encompasses literature review, threat modeling, method design, and experimental evaluation against baselines, within a

Qualifications

• Interest in privacy and security; differential privacy a plus.
• Experience with LLMs and solid Python skills.
• Knowledge of networking is a plus.

Responsibilities

• Review privacy-related literature and threat models in LLM systems.
• Define privacy leakage metrics and candidate network use cases.
• Design and prototype approaches to reduce leakage while preserving utility.
• Evaluate leakage reduction techniques against baselines in experiments.

Skills

Python

LLMs

Privacy

Differential privacy

Networking basics

Job description

Assignment

Context

Large language models are increasingly used to handle user requests that contain personal or sensitive information. While the model processes such a request, information from the input can be exposed beyond what is strictly needed to answer it. This raises concerns about privacy and data minimization.

Privacy in LLMs has been studied from several angles: memorization and extraction of training data [1], what it means for a language model to preserve privacy [2], contextual integrity in LLM behavior [3], and privacy-preserving reasoning [4].

Objectives

The internship will study how LLM-based systems can complete a user’s request while limiting the exposure of sensitive information contained in it, with network applications as the target domain. The main questions are:

• How can the information flows of an LLM-based system be modeled?
• How can privacy leakage be formally defined and measured?
• What mechanisms reduce leakage while preserving the quality of the result?

Work plan

• Review the literature on privacy in LLMs and on privacy-preserving mechanisms, such as differential privacy [5].
• Define a threat model and a privacy-leakage metric, and select a representative network use case.
• Design and implement an approach to reduce leakage.i<
• Evaluate it experimentally against baselines, along a privacy–utility trade-off.

Candidate profile

• Interest in privacy and security, with knowledge of differential privacy as a plus
• Experience with LLMs and solid Python skills
• Knowledge of networking is a plus

References

[1] N. Carlini et al., “Extracting Training Data from Large Language Models,” USENIX Security 2021.

[2] H. Brown et al., “What Does it Mean for a Language Model to Preserve Privacy?,” ACM FAccT 2022.

[3] N. Mireshghallah et al., “Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory,” ICLR 2024.

[4] “SALT: Steering Activations towards Leakage-free Thinking in Chain of Thought,” arXiv:2511.07772, 2025.

[5] C. Dwork and A. Roth, “The Algorithmic Foundations of Differential Privacy,” Foundations and Trends in Theoretical Computer Science, 2014.