mlsanityData to 7 Oct 2026

Spectre Attacks: Exploiting Speculative Execution

arXiv 1801.01203 ↗First posted 3 Jan 2018cs.CRcited by 47

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Abstract (arXiv)

Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access to the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, static analysis, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing/side-channel attacks. These attacks represent a serious threat to actual systems, since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 8; 2023: 3; 2024: 9; 2025: 11; 2026: 11.

47 papers.

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  1. 2021WaveFake: A Data Set to Facilitate Audio Deepfake Detectioncited by 78
  2. 2024On the Societal Impact of Open Foundation Modelscited by 64
  3. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  4. 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device MLcited by 21
  5. 2025Auditing Prompt Caching in Language Model APIscited by 19
  6. 2024LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trustcited by 18
  7. 2025Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inferencecited by 18
  8. 2022Secure Aggregation for Federated Learning in Flowercited by 15
  9. 2024Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interfacecited by 15
  10. 2021secureTF: A Secure TensorFlow Frameworkcited by 12
  11. 2025Dynamic Speculative Agent Planningcited by 10
  12. 2023HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestioncited by 8
35 more
  1. 2023ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patchingcited by 6
  2. 2024When Speculation Spills Secrets: Side Channels via Speculative Decoding In LLMscited by 6
  3. 2022Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniquescited by 5
  4. 2022Reward Delay Attacks on Deep Reinforcement Learningcited by 5
  5. 2021SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learningcited by 4
  6. 2025VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Tracescited by 4
  7. 2022Edge Security: Challenges and Issuescited by 3
  8. 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Modelscited by 3
  9. 2025Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challengescited by 3
  10. 2026Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Reviewcited by 3
  11. 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Supportcited by 2
  12. 2025SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chipcited by 2
  13. 2025From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teamingcited by 2
  14. 2026AgenticCache: Cache-Driven Asynchronous Planning for Embodied AI Agentscited by 2
  15. 2022A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automationcited by 1
  16. 2022Short Paper: Static and Microarchitectural ML-Based Approaches For Detecting Spectre Vulnerabilities and Attackscited by 1
  17. 2025Guillotine: Hypervisors for Isolating Malicious AIscited by 1
  18. 2025DR.FIX: Automatically Fixing Data Races at Industry Scalecited by 1
  19. 2026The Implications of Linguistic Illegibility for LLM Securitycited by 1
  20. 2021CloudShield: Real-time Anomaly Detection in the Cloudcited by 0
  21. 2022Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasurescited by 0
  22. 2022MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumptioncited by 0
  23. 2024Props for Machine-Learning Securitycited by 0
  24. 2024Differentially Private Learned Indexescited by 0
  25. 2024VerifiableFL: Verifiable Claims for Federated Learning using Exclavescited by 0
  26. 2025Towards Reinforcement Learning for Exploration of Speculative Execution Vulnerabilitiescited by 0
  27. 2025Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditingcited by 0
  28. 2026Emerging Threats and Countermeasures in Neuromorphic Systems: A Surveycited by 0
  29. 2026Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environmentscited by 0
  30. 2026ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verificationcited by 0
  31. 2026Shape and Substance: Dual-Layer Side-Channel Attacks on Local Vision-Language Modelscited by 0
  32. 2026uGen: An Agentic Framework for Generating Microarchitectural Attack PoCscited by 0
  33. 2026Ghost Tool Calls: Issue-Time Privacy for Speculative Agent Toolscited by 0
  34. 2026HiFuzz: Hierarchical Reinforcement Learning for Semantic-Aware and Adaptive CPU Fuzzingcited by 0
  35. 2026Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timingcited by 0