Hardware Vulnerabilities
Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.
"The vulnerabilities of hardware systems for training and inferencing brings issues to LLM-based applications."(p. 4)
Sub-categories (3)
Network Devices
"The training of LLMs often relies on distributed network systems [171], [172]. During the transmission of gradients through the links between GPU server nodes, significant volumetric traffic is generated. This traffic can be susceptible to disruption by burst traffic, such as pulsating attacks [161]. Furthermore, distributed training frameworks may encounter congestion issues [173]."
2.2 AI system security vulnerabilities and attacksGPU Computation Platforms
"The training of LLMs requires significant GPU resources, thereby introducing an additional security concern. GPU side-channel attacks have been developed to extract the parameters of trained models [159], [163]."
2.2 AI system security vulnerabilities and attacksMemory and Storage
"Similar to conventional programs, hardware infrastructures can also introduce threats to LLMs. Memory-related vulnerabilities, such as rowhammer attacks [160], can be leveraged to manipulate the parameters of LLMs, giving rise to attacks such as the Deephammer attack [167], [168]."
2.2 AI system security vulnerabilities and attacksOther risks from Cui et al. (2024) (49)
Harmful Content
1.2 Exposure to toxic contentHarmful Content > Bias
1.1 Unfair discrimination and misrepresentationHarmful Content > Toxicity
1.2 Exposure to toxic contentHarmful Content > Privacy Leakage
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationUntruthful Content
3.1 False or misleading informationUntruthful Content > Factuality Errors
3.1 False or misleading information