# Time Will Tell: Timing Side Channels via Output Token Count in Large Language Models

arXiv: https://arxiv.org/abs/2412.15431 · HTML: https://mlsanity.com/paper/2412.15431/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2024.

## Built on by (most cited first, 7 of 7)

- [Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference](https://mlsanity.com/paper/2508.08438/) (2025)
- [Whisper Leak: a side-channel attack on Large Language Models](https://mlsanity.com/paper/2511.03675/) (2025)
- [Spill The Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models](https://mlsanity.com/paper/2505.00817/) (2025)
- [Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model](https://mlsanity.com/paper/2602.07878/) (2026)
- [OptiLeak: Efficient Prompt Reconstruction via Reinforcement Learning in Multi-tenant LLM Services](https://mlsanity.com/paper/2602.20595/) (2026)
- [Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timing](https://mlsanity.com/paper/2607.20723/) (2026)
- [SparLeak: Privacy Leakage from Sparse Attention in LLM Inference on Shared GPUs](https://mlsanity.com/paper/2609.38830/) (2026)

## Abstract (arXiv)

This paper demonstrates a new side-channel that enables an adversary to extract sensitive information about inference inputs in large language models (LLMs) based on the number of output tokens in the LLM response. We construct attacks using this side-channel in two common LLM tasks: recovering the target language in machine translation tasks and recovering the output class in classification tasks. In addition, due to the auto-regressive generation mechanism in LLMs, an adversary can recover the output token count reliably using a timing channel, even over the network against a popular closed-source commercial LLM. Our experiments show that an adversary can learn the output language in translation tasks with more than 75% precision across three different models (Tower, M2M100, MBart50). Using this side-channel, we also show the input class in text classification tasks can be leaked out with more than 70% precision from open-source LLMs like Llama-3.1, Llama-3.2, Gemma2, and production models like GPT-4o. Finally, we propose tokenizer-, system-, and prompt-based mitigations against the output token count side-channel.