Sina Kamali (University of Waterloo), Diogo Barradas (University of Waterloo)

As Internet censorship grows pervasive, users often rely on covert channels to evade surveillance and access restricted content. Web protocol tunneling tools use websites as proxies, encapsulating covert data within web protocols to blend with legitimate traffic to avoid detection. However, existing tools are prone to detection via traffic analysis, enabling censors to identify the use of such tools via fingerprinting attacks or due to the generation of abnormal browsing patterns.

We present Huma, a new web protocol tunneling tool that addresses existing detection concerns. By deferring covert data transmissions, Huma allows a website participating in circumvention to first respond with unmodified content, while responses embedding covert data are prepared in the background and delivered during the client's next request, thus avoiding timing anomalies that facilitate fingerprinting. By relying on an overt user simulator modeled after realistic browsing activity, Huma also follows users' expected browsing behaviors. Lastly, Huma prevents adversary-controlled websites from tying communication endpoints together, enabling straightforward extensions to enable covert communications in Intranet censorship scenarios.

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NeuroStrike: Neuron-Level Attacks on Aligned LLMs

Lichao Wu (Technical University of Darmstadt), Sasha Behrouzi (Technical University of Darmstadt), Mohamadreza Rostami (Technical University of Darmstadt), Maximilian Thang (Technical University of Darmstadt), Stjepan Picek (University of Zagreb & Radboud University), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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TENSURE: Fuzzing Sparse Tensor Compilers (Registered Report)

Kabilan Mahathevan (Department of Computer Science, Virginia Tech, Blacksburg), Yining Zhang (Department of Computer Science, Virginia Tech, Blacksburg), Muhammad Ali Gulzar (Department of Computer Science, Virginia Tech, Blacksburg), Kirshanthan Sundararajah (Department of Computer Science, Virginia Tech, Blacksburg)

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NOD: Uncovering intense attackers’ behavior through Nested Outlier Detection...

Ghazal Abdollahi (University of Utah), Hamid Asadi (University of Utah), Robert Ricci (University of Utah)

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