Architectural Backdoors for Within-Batch Data Stealing and Model
  Inference Manipulation
For nearly a decade the academic community has investigated backdoors in
neural networks, primarily focusing on classification tasks where adversaries
manipulate the model prediction. While demonstrably malicious, the immediate
real-world impact of such prediction-altering attacks has remained unclear. In
this paper we introduce a novel and significantly more potent class of
backdoors that builds upon recent advancements in architectural backdoors. We
demonstrate how these backdoors can be specifically engineered to exploit
batched inference, a common technique for hardware utilization, enabling
large-scale user data manipulation and theft. By targeting the batching
process, these architectural backdoors facilitate information leakage between
concurrent user requests and allow attackers to fully control model responses
directed at other users within the same batch. In other words, an attacker who
can change the model architecture can set and steal model inputs and outputs of
other users within the same batch. We show that such attacks are not only
feasible but also alarmingly effective, can be readily injected into prevalent
model architectures, and represent a truly malicious threat to user privacy and
system integrity. Critically, to counteract this new class of vulnerabilities,
we propose a deterministic mitigation strategy that provides formal guarantees
against this new attack vector, unlike prior work that relied on Large Language
Models to find the backdoors. Our mitigation strategy employs a novel
Information Flow Control mechanism that analyzes the model graph and proves
non-interference between different user inputs within the same batch. Using our
mitigation strategy we perform a large scale analysis of models hosted through
Hugging Face and find over 200 models that introduce (unintended) information
leakage between batch entries due to the use of dynamic quantization.
683573bc0830dfc67834f1b5
Nicolas Küchler
683573bc0830dfc67834f1b6
Ivan Petrov
683573bc0830dfc67834f1b7
Conrad Grobler
683573bc0830dfc67834f1b8
Ilia Shumailov
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2025-05-27T12:41:35.703Z
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Architectural Backdoors for Within-Batch Data Stealing and Model
  Inference Manipulation
For nearly a decade the academic community has investigated backdoors in
neural networks, primarily focusing on classification tasks where adversaries
manipulate the model prediction. While demonstrably malicious, the immediate
real-world impact of such prediction-altering attacks has remained unclear. In
this paper we introduce a novel and significantly more potent class of
backdoors that builds upon recent advancements in architectural backdoors. We
demonstrate how these backdoors can be specifically engineered to exploit
batched inference, a common technique for hardware utilization, enabling
large-scale user data manipulation and theft. By targeting the batching
process, these architectural backdoors facilitate information leakage between
concurrent user requests and allow attackers to fully control model responses
directed at other users within the same batch. In other words, an attacker who
can change the model architecture can set and steal model inputs and outputs of
other users within the same batch. We show that such attacks are not only
feasible but also alarmingly effective, can be readily injected into prevalent
model architectures, and represent a truly malicious threat to user privacy and
system integrity. Critically, to counteract this new class of vulnerabilities,
we propose a deterministic mitigation strategy that provides formal guarantees
against this new attack vector, unlike prior work that relied on Large Language
Models to find the backdoors. Our mitigation strategy employs a novel
Information Flow Control mechanism that analyzes the model graph and proves
non-interference between different user inputs within the same batch. Using our
mitigation strategy we perform a large scale analysis of models hosted through
Hugging Face and find over 200 models that introduce (unintended) information
leakage between batch entries due to the use of dynamic quantization.
A novel class of backdoors in neural network architectures exploits batched inference to enable large-scale data manipulation, demonstrating information leakage and control over user inputs and outputs, with a proposed mitigation strategy using Information Flow Control.
backdoors neural networks classification tasks batched inference hardware utilization information leakage mitagation strategy Information Flow Control Hugging Face dynamic quantization