Side-Channel Attacks exploit unintended information leakage through observable physical or logical system behaviors such as memory usage, timing information, power consumption, or electromagnetic emissions. Rather than directly querying the model, these attacks monitor and analyze indirect signals generated during model execution. For example, by observing memory access patterns or execution times, attackers can infer the architecture of neural networks, including the number of layers and neurons. These attacks are particularly powerful because they can extract information even when direct query access is limited or monitored, and they often require less queries than traditional API-based approaches.

What Are AI Side-Channel Attacks?

A side-channel attack is a security exploit that targets information gained from the implementation of a system, rather than attacking the system’s functionality directly. These attacks extract sensitive information by observing the indirect physical effects of a device’s operation, rather than exploiting software vulnerabilities or cryptographic weaknesses. They focus on unintended information leakage through physical or observable characteristics of the system’s operation – such as timing, power consumption, electromagnetic emissions, or sound. Unlike traditional attacks that exploit logical or algorithmic vulnerabilities, side-channel attacks exploit the physical reality of the system’s implementation to uncover secrets such as cryptographic keys, passwords, personal data, or in the case of ML models, architecture details and parameters.

Side-channel attacks work by monitoring and analyzing indirect, auxiliary data produced during normal system operation. The core principle is that physical systems inevitably leak information about their internal states and operations through various measurable phenomena. These leaks often go unnoticed by system designers who focus primarily on functional correctness rather than information security. Most side-channel attacks are non-invasive – they don’t require opening up the device or physically tampering with it. Instead, attackers use external sensors, software, or statistical methods to collect and analyze leaked information. Some attacks need only a single observation, while others aggregate data from thousands of operations to build a clear picture. Advanced side-channel attacks have successfully extracted neural network architectures by analyzing electromagnetic signals emitted during inference or by observing power consumption patterns.

How Are Side-Channel AI Model Extraction Attacks Implemented?

Side-channel model extraction attacks are practical with either software orhardware access, and cloud computing and shared hardware environments have made side-channel attacks more relevant than ever – attackers can spy on co-located users without physical access, while edge device attacks are particularly concerning because they leverage indirect physical observations of computing systems at the network edge to extract sensitive information about AI models without leaving digital traces.

Software Access-Based Attacks

What makes software-based side channels particularly dangerous is their accessibility and scalability. Unlike hardware-based attacks requiring specialized equipment, software side channels can be launched from ordinary user accounts, virtual machines, or containers sharing hardware with the target system. In cloud computing environments where multiple tenants share the same physical infrastructure, these attacks can potentially extract proprietary model details across security boundaries that were previously thought to be robust. Furthermore, software side-channel techniques can often be automated and scaled to systematically extract information about multiple target models or to improve extraction accuracy through statistical aggregation of multiple observations. Defenses against these attacks are particularly challenging because they often exploit fundamental performance optimizations in modern computing systems, creating a tension between security and efficiency.

Hardware Access-Based Attacks

Hardware access based side-channel attacks represent a powerful class of extraction techniques that directly observe and analyze the physical characteristics of computing devices running machine learning models. These attacks are particularly effective because they tap into fundamental physical phenomena that are difficult to hide without significant performance penalties. When an adversary gains physical proximity to or direct access to the hardware running an ML model, they can deploy a range of sophisticated measurement techniques to extract information that would be inaccessible through software interfaces alone. Power consumption analysis measures the electrical current drawn by processors during computation, with different operations creating distinctive power signatures. For neural networks, matrix multiplications, activation functions, and data movement operations, each produces characteristic power consumption patterns that can reveal architectural details. Researchers have demonstrated that by capturing high-resolution power traces during model inference, an attacker can reconstruct complete neural network parameters, including precise weight values, particularly for models deployed on edge devices or embedded systems.

What Are The Types Of AI Side-Channel Attacks?

See below for a list and description of side-channel attacks types in alphabetical order, from Acoustic Side-Channel Attacks to Whitelist-Based Side-Channel Attacks.

Acoustic Side-Channel Attacks
Allocation-Based Side-Channel Attacks
Cache Side-Channel Attacks
Data Remanence
Differential Fault Analysis
Electromagnetic (EM) Attacks
Hardware-Based Side-Channels
Network Traffic Analysis
Optical & Thermal Side-Channel Attacks
Power Analysis Attacks
Software-Initiated Fault Attacks
System Resource Leakage
Timing Attacks
Whitelist-Based Side-Channel Attacks

1. Acoustic Side-Channel Attacks

Acoustic side-channel attacks exploit sound emissions from devices to infer sensitive operations or data. Electronic devices emit subtle but measurable sounds during operation, including keyboard clicks, coil whine from power components, fan noise variations, or vibrations from internal components like hard drives. Attackers can record these sounds using microphones and apply signal processing or machine learning techniques to identify patterns that correlate with specific operations or data being processed. For example, different keystrokes on a keyboard produce slightly different sounds, allowing attackers to reconstruct typed passwords. In computing systems, processor and memory operations create distinctive acoustic signatures that vary based on the workload. For ML model extraction, acoustic attacks can reveal information about the computational intensity and patterns during model inference. The sounds produced by cooling fans ramping up during intensive matrix multiplications, or the coil whine from power delivery components under varying loads, can expose the sequence and nature of operations in the model. These acoustic patterns could potentially reveal which parts of a neural network are most computationally intensive, providing insights about the model’s architecture and operation sequence. While acoustic attacks typically require proximity to the target device, modern microphones are highly sensitive, and research has shown that attacks can be effective from several meters away or even through video conferencing systems that capture audio. This makes acoustic side-channels a viable vector for model extraction in scenarios where attackers have periodic physical proximity to ML inference systems.

2. Allocation-Based Side-Channel Attacks

Allocation-based side-channel attacks exploit information leaked through the allocation and management of shared resources, such as network bandwidth, memory, compute units, or I/O operations, in multi-user environments. In systems where resources are shared among multiple users or processes, the patterns of resource allocation can reveal information about other users’ activities. For example, in cloud environments, virtual machines sharing the same physical hardware may experience performance variations based on neighboring workloads. By monitoring how resources like CPU time, memory bandwidth, or network throughput fluctuate, attackers can infer activity patterns of co-located users. In the context of ML model extraction, allocation-based attacks can reveal valuable information about model architecture and complexity by observing resource usage patterns during inference. The amount of memory allocated, GPU utilization patterns, or bandwidth consumption can indicate model size, complexity, and computational requirements. For instance, by measuring memory allocation during ML service initialization, attackers might determine approximate model parameter counts. Similarly, observing how CPU or GPU resources are utilized over time during inference can reveal the sequential structure of operations in a neural network. In multi-tenant environments hosting ML services, these attacks could allow competitors to estimate the computational requirements and complexity of proprietary models. Cloud-based ML services might unintentionally leak information about batch processing capabilities or optimization levels through observable resource allocation patterns. These attacks are particularly relevant in shared cloud environments, edge computing scenarios with multi-tenant deployments, or any situation where ML resources are divided among multiple users. They highlight the importance of resource isolation and consistent resource allocation patterns when deploying sensitive ML models in shared infrastructure.

3. Cache Side-Channel Attacks

Cache side-channel attacks exploit the way shared memory caches are accessed in multi-user or virtualized environments to leak information about other users’ operations. Modern CPUs use hierarchical caches to speed up memory access, and these caches are often shared among different processes or even different users in cloud environments. By observing how these shared caches are used, attackers can infer what memory addresses other processes are accessing. Techniques like PRIME+PROBE involve first filling a cache with the attacker’s data, waiting for the victim to execute, and then measuring which of the attacker’s data has been evicted from the cache, revealing the victim’s access patterns. FLUSH+RELOAD works by flushing specific memory lines from the cache and later checking if the victim has reloaded them. These techniques allow precise monitoring of a victim’s memory access patterns, which directly correlate with code execution paths and data being processed. In ML model extraction, cache attacks can reveal which parts of a model are activated during inference, potentially exposing neural network structure, weight matrices sizes, and activation patterns. For example, by monitoring cache accesses during inference, attackers can determine which branches of a decision tree are taken or which neurons in a neural network are activated for specific inputs. Cache attacks are particularly dangerous in cloud environments where ML models might be deployed as services, as they can be executed entirely in software without special hardware or physical access, making them applicable in shared hosting scenarios where different users’ workloads run on the same physical hardware.

4. Data Remanence

Data remanence attacks exploit the fact that data doesn’t immediately disappear from memory when power is removed, allowing attackers to recover sensitive information from supposedly cleared memory. RAM, contrary to common belief, doesn’t lose its contents instantly when powered off, especially at lower temperatures. In cold boot attacks, attackers quickly reboot a system into a minimal operating system designed to dump memory contents, or physically remove memory modules and transfer them to a separate system for reading. This technique can recover cryptographic keys, passwords, and other sensitive data that remains in memory after normal operation. For ML model extraction, data remanence attacks can potentially recover complete model parameters and architecture information that was loaded into memory during inference or training. Modern ML frameworks often keep entire models in memory for performance reasons, making them vulnerable to memory extraction. The rise of large language models and other parameter-heavy AI systems increases the attractiveness of this attack vector, as valuable intellectual property remains resident in memory. Even partial memory recovery can be valuable, as neural network weights often have patterns that allow reconstruction of missing values. Data remanence concerns extend beyond RAM to other storage mediums like SSDs, where wear-leveling and garbage collection mechanisms may retain copies of sensitive data even after deletion. These attacks typically require physical access to the target system, making them most relevant for edge devices or physical server installations, but they represent a significant risk in scenarios where attackers might gain temporary physical access to AI inference systems.

5. Differential Fault Analysis

Differential fault analysis involves deliberately introducing faults or errors into a device’s computations and analyzing the resulting incorrect outputs to extract secrets. Unlike passive side-channel attacks that merely observe leakage, fault attacks actively interfere with a system’s operation to induce informative errors. Attackers may use techniques such as voltage manipulation (power glitching), clock glitching, electromagnetic pulses, laser irradiation, or temperature manipulation to cause hardware to malfunction in specific, controllable ways. By comparing correct outputs with those produced when faults are introduced, attackers can deduce information about internal states and secret values. For example, in cryptographic systems, carefully timed faults can reveal key bits. In ML model extraction, fault injection can be used to reveal model parameters and architecture details. By introducing faults during specific operations and observing how the model’s outputs change, attackers can infer information about weight values and activation functions. Research has demonstrated that by inducing faults in specific neurons or layers of neural networks through voltage glitching, attackers can systematically extract weight values by observing output differences. These attacks are particularly effective against quantized neural networks where parameter values have limited precision. Some fault attacks target the fault-tolerance mechanisms themselves, which may reveal more about the internal structure than the primary computation. Fault attacks require some level of physical access to the device, making them most relevant for edge AI deployments, embedded systems, and IoT devices.

6. Electromagnetic (EM) Attacks

Electromagnetic attacks capture and analyze electromagnetic emissions from electronic devices to extract sensitive data. All electronic circuits emit electromagnetic radiation during operation, with the patterns of these emissions corresponding to the operations being performed and the data being processed. EM attacks use specialized antennas or probes to capture these emissions, which can then be analyzed using signal processing techniques to extract patterns related to secret information. These attacks are similar to power analysis but can be performed at a distance without physical contact with the target device, making them more stealthy and versatile. EM emissions can be captured through walls, making them effective even when devices are physically secured. In ML contexts, EM attacks have been successfully used to extract neural network parameters, architecture details, activation functions, and even training data characteristics. Research has demonstrated complete extraction of neural network weights by capturing EM signals during inference operations. Different operations within neural networks, such as matrix multiplications, pooling, and activation functions, produce distinctive EM signatures that can reveal the network’s structure. EM attacks are particularly concerning for edge AI deployments, as they combine the information leakage potential of power analysis with the ability to be performed non-invasively at a distance, potentially allowing attackers to extract model information without detection.

7. Hardware-Based Side-Channels

Hardware-based side channels encompass a range of attack vectors that exploit various aspects of hardware implementation to extract sensitive information. PCIe Traffic Monitoring targets the data transfers between central processing units (CPUs) and graphics processing units (GPUs), which are often unencrypted even when the computational data itself is protected. By observing these transfers during model execution, attackers can potentially reconstruct model architecture and parameters, particularly for ML frameworks that offload computations to GPUs. FPGA Resource Exploitation leverages vulnerabilities in multi-tenant field-programmable gate array (FPGA) environments, where multiple users share the same reconfigurable hardware. Since FPGAs share power distribution systems, clock networks, and thermal characteristics, one tenant can potentially observe the activity of others through measurement of these shared resources. As FPGAs become increasingly popular for ML acceleration, these attacks pose a growing risk for model extraction. Memory Access Pattern Monitoring observes how systems access memory, even when the memory contents themselves are encrypted. Through techniques like memory deduplication analysis in virtualized or cloud environments, attackers can determine when identical memory pages are being used, potentially revealing neural network structure and parameter patterns. The timing and sequence of memory accesses can expose the execution flow of ML algorithms, even without seeing the actual data being processed.

8. Network Traffic Analysis

Network traffic analysis exploits patterns in the communication between clients and ML services to extract information about model architecture, complexity, and behavior without direct access to the model itself. Packet Size and Timing analysis examines the volume and temporal patterns of data transmitted between clients and ML services during inference or training. Even when packet contents are encrypted, metadata such as packet sizes, timing, and sequencing can reveal significant information about model operations. For example, the size of responses might correlate with model complexity or the confidence levels of predictions, while timing patterns could reveal batch processing behavior or computational bottlenecks in the model architecture. Data Compression Patterns provide another avenue for leakage, as different types of model outputs may compress differently when transmitted over networks. Variations in compression efficiency across different outputs can potentially reveal information about the underlying distributions or structures in the model’s outputs. By monitoring how response sizes change for different inputs, attackers might infer which inputs generate more complex or information-rich outputs from the model. In distributed ML systems, the communication patterns between nodes can reveal the distributed architecture and workload distribution strategy. The frequency and volume of synchronization messages might expose training batch sizes or parameter update methods. For federated learning systems, analysis of update transmissions could potentially leak information about local model adaptations. These network-based side channels are particularly relevant for cloud-based ML services, APIs, and distributed training systems, where network communication is an essential component of operation. They highlight the importance of considering not just the security of model parameters themselves, but also the patterns in how models communicate and respond to different inputs.

9. Optical & Thermal Side-Channel Attacks

Optical and thermal side-channel attacks use cameras, infrared sensors, or other optical devices to observe visible light or heat emissions from hardware, revealing sensitive operations. Electronic devices generate heat during operation, with different components heating up based on their workload. Similarly, indicators like LED activity lights, display emissions, or even the reflections of screen contents can leak information. Attackers can use thermal cameras to create heat maps of devices during operation, revealing which components are active at different times. High-end thermal cameras can detect temperature differences as small as 0.025°C, allowing detailed analysis of chip activity. In some cases, even consumer-grade smartphone thermal cameras provide sufficient resolution for these attacks. Optical attacks might monitor the blinking patterns of hard drive or network activity LEDs, which correspond to data access patterns, or capture faint light emissions from transistors switching (photonic emissions) that directly correlate with the data being processed. For ML model extraction, thermal imaging can reveal which parts of a processor or accelerator are active during inference, potentially mapping out the neural network’s structure based on heat generation patterns. Different operations like matrix multiplications, activation functions, or memory accesses create distinctive thermal signatures that can be analyzed to determine the sequence and nature of operations. By correlating these thermal patterns with specific inputs, attackers might infer which sections of a model are most computationally intensive for different input types, revealing architectural details. While these attacks typically require line-of-sight access to the target hardware, they can be concerning in scenarios like edge AI deployments in public spaces, shared server rooms, or kiosk systems where attackers might briefly position thermal or optical sensors near the target device.

10. Power Analysis Attacks

Power analysis attacks monitor a device’s power consumption during computations to extract sensitive data, such as encryption keys or model parameters. These attacks exploit the fact that electronic circuits consume varying amounts of power depending on the operations they perform and the data they process. There are two main variants: Simple Power Analysis (SPA) directly observes power usage patterns to identify operations and data being processed, while Differential Power Analysis (DPA) applies statistical methods to many power traces to extract correlations with secret information, making it more powerful against noise and countermeasures. Power analysis requires physical access to the device or its power supply, making it particularly relevant for edge devices, IoT systems, and embedded systems. In the context of ML models, power analysis can reveal which operations are being performed during inference, potentially exposing the model architecture, activation functions, and even weight values. For example, researchers have demonstrated extracting complete neural network parameters from embedded devices by analyzing power consumption during forward propagation.

11. Software-Initiated Fault Attacks

Software-initiated fault attacks exploit hardware vulnerabilities through software means, allowing attackers to induce hardware errors without physical access to the device. The most well-known example is Rowhammer, which exploits a fundamental weakness in modern DRAM. By repeatedly accessing certain rows in memory (“hammering” them), attackers can cause bit flips in adjacent rows due to electrical interference between tightly packed memory cells. Unlike traditional fault attacks requiring specialized equipment, Rowhammer can be executed entirely in software, making it accessible to remote attackers. These attacks can bypass memory protection mechanisms, potentially altering protected data like cryptographic material or privilege levels. In ML model extraction contexts, Rowhammer and similar attacks have been adapted to target quantized neural networks stored in memory. By strategically flipping bits in model parameters and observing the resulting changes in output, attackers can systematically extract information about weight values. This approach is particularly effective against binary neural networks or other quantized models where individual bit flips cause significant, observable output changes. The software-based nature of these attacks makes them relevant even in cloud environments where physical access is restricted. They represent a concerning evolution of fault analysis techniques that brings previously hardware-dependent attacks into the realm of remote exploitation. Research has demonstrated successful extraction of neural network parameters using Rowhammer-induced faults, showing how even memory-safe programming languages and sandboxed execution environments may not protect against hardware-level vulnerabilities exploited through software means.

12. System Resource Leakage

System resource leakage refers to information inadvertently disclosed through observable patterns in how a system utilizes various computational resources during operation. Memory Usage Monitoring tracks how memory is allocated and accessed during model inference, potentially revealing the size of weight matrices, activation maps, and overall model architecture. Even when memory contents are encrypted, the patterns of allocation and deallocation can indicate the sequence and scale of operations being performed. CPU/GPU Utilization patterns expose which processing units are active at different stages of inference, with spikes in usage corresponding to computationally intensive operations like convolutions or matrix multiplications in neural networks. The timing and intensity of these utilization patterns can reveal the structure and complexity of different model components. Thermal Signatures from processors, memory, and other components reflect computational workloads, with different operations generating distinctive heat patterns that can be monitored to map model execution flow. Batch Processing Indicators reveal how systems handle multiple inference requests, potentially exposing underlying hardware configurations, parallelization capabilities, and resource allocation strategies. The way performance scales with batch size can indicate whether a model uses batching optimizations and what their limitations might be. In multi-tenant environments, these system resource patterns may leak valuable information about proprietary model architectures and optimizations without requiring direct access to the model parameters themselves. For example, by observing how GPU memory usage scales with input size, attackers might deduce the dimensions of internal layers in a neural network. Similarly, the pattern of CPU core utilization during inference could reveal whether a model uses threading optimizations or specialized instructions. These leakages are particularly relevant for edge AI deployments and cloud-based ML services, where monitoring system resources might be possible through management interfaces, shared infrastructure, or external observation of device behavior.

13. Timing Attacks

Timing attacks exploit variations in the time it takes a device or algorithm to perform operations, especially cryptographic computations or model inference. These attacks are based on the fundamental principle that different operations take different amounts of time to execute, and these time differences can reveal sensitive information. For example, in password verification systems, the code might return earlier if the first character is wrong, allowing an attacker to determine each character of a password sequentially. In the context of machine learning models, timing attacks can reveal significant architectural details by measuring inference latency. Complex operations like matrix multiplications in neural networks have execution times proportional to their dimensions, potentially exposing layer sizes. Similarly, specialized components like attention mechanisms in transformers or branch operations in decision trees can produce distinctive timing patterns. Timing attacks are particularly dangerous because they can often be performed remotely over networks, making them accessible to attackers without physical access to the target device. They have been successfully demonstrated against various ML models, revealing information about model architecture, optimization techniques, and even training data characteristics. The precision of modern processors’ timing capabilities makes these attacks increasingly effective, allowing attackers to measure minute differences in execution time with high accuracy.

14. Whitelist-Based Side-Channel Attacks

Whitelist-based side-channel attacks exploit differences in how systems respond to known (whitelisted) versus unknown entities to track or identify targets. Many systems maintain lists of recognized or trusted entities and behave differently when interacting with entries on these lists compared to strangers. These behavioral differences create observable side-channels that attackers can leverage. For example, Bluetooth devices often respond differently to connection attempts from previously paired devices versus new ones, allowing tracking even when communications are encrypted or MAC addresses are randomized. In ML service contexts, whitelist-based attacks might exploit differences in how APIs respond to different client identifiers or query patterns. ML services often implement tiered access mechanisms, rate limiting, or specialized optimization for recognized clients. By observing subtle differences in response times, error messages, or throttling behaviors, attackers can determine if certain clients receive preferential treatment, potentially identifying high-value customers or internal users. This information can be used to target specific clients for impersonation or to craft more convincing social engineering attacks. In model extraction scenarios, whitelist-based patterns might reveal which query patterns are considered legitimate versus suspicious, helping attackers design extraction strategies that remain below detection thresholds. For instance, if a service provides more detailed outputs or higher precision to trusted clients, attackers might attempt to manipulate their request patterns to appear trusted. These attacks are primarily relevant to networked ML services rather than local models, but they highlight how access control mechanisms themselves can sometimes leak information that aids in model extraction or other attacks.

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