Flush+Flush: A Fast and Stealthy Cache Attack
Not on the map. Why?
Abstract (arXiv)
Research on cache attacks has shown that CPU caches leak significant information. Proposed detection mechanisms assume that all cache attacks cause more cache hits and cache misses than benign applications and use hardware performance counters for detection. In this article, we show that this assumption does not hold by developing a novel attack technique: the Flush+Flush attack. The Flush+Flush attack only relies on the execution time of the flush instruction, which depends on whether data is cached or not. Flush+Flush does not make any memory accesses, contrary to any other cache attack. Thus, it causes no cache misses at all and the number of cache hits is reduced to a minimum due to the constant cache flushes. Therefore, Flush+Flush attacks are stealthy, i.e., the spy process cannot be detected based on cache hits and misses, or state-of-the-art detection mechanisms. The Flush+Flush attack runs in a higher frequency and thus is faster than any existing cache attack. With 496 KB/s in a cross-core covert channel it is 6.7 times faster than any previously published cache covert channel.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 1; 2025: 0; 2026: 3.
10 papers.
- 2023HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestioncited by 8
- 2021A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Imagescited by 1
- 2021CloudShield: Real-time Anomaly Detection in the Cloudcited by 0
- 2022MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumptioncited by 0
- 2022Design of secure and robust cognitive system for malware detectioncited by 0
- 2023Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classificationcited by 0
- 2024Attacking Slicing Network via Side-channel Reinforcement Learning Attackcited by 0
- 2026uGen: An Agentic Framework for Generating Microarchitectural Attack PoCscited by 0
- 2026SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channelscited by 0
- 2026Detokenization Leaks: Reconstructing Local LLM Outputs From Cache Tracescited by 0
- 2026Detokenization Leaks: Reconstructing Local LLM Outputs From Cache Traces6 Sep 2026
- 2026SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels4 Aug 2026
- 2026uGen: An Agentic Framework for Generating Microarchitectural Attack PoCs15 May 2026
- 2024Attacking Slicing Network via Side-channel Reinforcement Learning Attack17 Sep 2024
- 2023HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion21 Dec 2023
- 2023Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classification4 Apr 2023
- 2022Design of secure and robust cognitive system for malware detection3 Aug 2022
- 2022MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption31 May 2022
- 2021CloudShield: Real-time Anomaly Detection in the Cloud20 Aug 2021
- 2021A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images14 Apr 2021