Sponge Examples: Energy-Latency Attacks on Neural Networks (2006.03463v2)

Abstract: The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural networks in datacenters and deploy them on edge devices, the focus so far is on average-case performance. In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully crafted $\boldsymbol{sponge}~\boldsymbol{examples}$, which are inputs designed to maximise energy consumption and latency. We mount two variants of this attack on established vision and LLMs, increasing energy consumption by a factor of 10 to 200. Our attacks can also be used to delay decisions where a network has critical real-time performance, such as in perception for autonomous vehicles. We demonstrate the portability of our malicious inputs across CPUs and a variety of hardware accelerator chips including GPUs, and an ASIC simulator. We conclude by proposing a defense strategy which mitigates our attack by shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective.

• The paper introduces a novel threat vector by crafting sponge examples that substantially increase energy consumption and processing delays in neural networks.
• The study employs both gradient-based and genetic algorithm methods across white-box and black-box settings, demonstrating vulnerabilities up to 30x and even 6000x in latency.
• The findings advocate for shifting analytical frameworks from average-case to worst-case scenarios to strengthen defenses against availability attacks in ML systems.

Analysis of "Sponge Examples: Energy-Latency Attacks on Neural Networks"

The paper "Sponge Examples: Energy-Latency Attacks on Neural Networks" presents a novel methodology to exploit the energy consumption and decision latency vulnerabilities of neural networks. The authors introduce the concept of "sponge examples," which are specially crafted inputs designed to maximize the energy usage and processing time of neural networks, thereby driving such systems to their worst-case performance scenarios. This research primarily addresses the availability aspect of ML security, adding a new dimension to the traditional confidentiality and integrity-focused security triads.

Key Contributions and Findings

1. Novel Threat Vector: The authors identify a new denial-of-service attack vector targeting the energy and latency performance of ML models. Sponge examples significantly elevate both metrics, providing the first method to explicitly attack these aspects.
2. Vulnerability of LLMs: The results indicate that LLMs, in particular, show a high susceptibility to sponge examples, showcasing a latency and energy consumption increase by factors as high as 30x.
3. Cross-Platform and Cross-Architecture Portability: Sponge examples have demonstrated portability across various hardware platforms and architectures, including CPUs, GPUs, and an ASIC simulator, as well as different neural network models and tasks. For instance, on Microsoft's Azure Translator, a sponge example resulted in a latency deterioration by a factor of 6000x.
4. Defense Strategy Proposal: The paper suggests a preventive measure by shifting analytical strategies from average-case to worst-case scenarios, thereby ensuring more robust system defenses against such availability attacks.

Methodological Framework

The authors employ both gradient-based and genetic algorithm approaches to craft sponge examples. The prior requires access to model parameters (White-box setting), while the latter operates independently of such specifics (Black-box setting). This enables the researchers to test their theory across different threat models, applicable to various real-world deployments of ML.

Experimental Verification

Extensive experiments highlight the effectiveness of these attacks on a broad set of tasks including NLP and computer vision benchmarks. Within NLP, energy penalties are linked to inefficiencies in sentence representations, emphasizing the computational demand driven by high-dimensional token embeddings. In vision tasks, although the effect is less pronounced, sponge examples still manage to increase computation density marginally, particularly in models exploiting data sparsity at a hardware level.

Implications of Research

The findings call for immediate attention to energy consumption as a critical aspect of neural network security. With the ever-increasing adoption of edge devices and ML-as-a-Service offerings, this attack vector might notably impact both economic models (by inflating operational costs) and functional reliability (by potentially crippling time-sensitive applications).

By transitioning hardware analysis frameworks to incorporate worst-case scenarios, the practical deployment of machine learning systems can be made more resilient to such attacks. As these sponge examples could conceivably be adapted to other forms of application-layer attacks, a comprehensive understanding and mitigation strategy become crucial for maintaining the robustness and availability of future AI systems.

Future Research

While this paper pioneers the domain of energy-latency attacks, it remains crucial to examine further iterative optimizations of sponge examples and potential countermeasures more deeply. As part of wider security assessments, integrating sponge example detection within system architectures could mitigate these threats across emerging AI platforms.

In conclusion, the notion of sponge examples reimagines the landscape of adversarial machine learning, urging us to rethink how we assess and shield ML-driven technologies against energy-based vulnerabilities. As such, it establishes a foundational step towards a comprehensive defense framework for AI systems, safeguarding against an expanded array of adversarial exploitation techniques.