cs.CR · 2026-09-21 · No. 120
Cryptography and Security, 2026-09-21.
11 new papers in cs.CR. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
11 entries-
01
End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient Sign Recovery
Akira Ito, Takayuki Miura, Yosuke Todo
cs.CR · cs.LG
The importance of deep neural networks (DNNs) is widely recognized, and the parameters obtained through training are regarded as valuable assets. Recently, attacks that extract these parameters using only oracle queries to a DNN have been actively studied at IACR conferences. The hard-label setting is the most challenging setting for model extraction, where an adversary can observe only the final output label, such as "dog" or "cat." At...
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02
Watermarkable Multi-Draft Speculative Sampling via Poisson Processes
Yanxiao Liu, Sicheng Wan, Zhan Gao, Deniz Gündüz
cs.CR · cs.LG
Large language models (LLMs) have achieved state-of-the-art performance across a wide range of tasks, motivating two important aspects of deployment: inference efficiency and output provenance, which can be tackled by speculative sampling and watermarking, respectively. However, recent works have shown that combining these two goals is highly nontrivial and can be potentially impossible. In this work, we develop a novel multi-draft...
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03
Verifiable Computation with Trusted Execution Environments and On-Chain Digital Rights Tokens
Bingle Stegmann Kruger, Co-Pierre Georg
cs.CR · cs.DC
We present an architecture that enables data owners to combine private data into data pools using Trusted Execution Environments (TEEs) and manage these pools by issuing narrowly scoped computational rights, encoded as Digital Rights Tokens (DRTs), to third-party data analysts. Each DRT binds specific open-source code to a pool and is issued and redeemed on a distributed ledger. Data analysts can obtain the right to execute open-source code...
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04
TERMon: Detecting Persistent Behavioral Threats in Edge AI via Hardware-Native Ternary Runtime Monitor
Arish Sateesan, Edlira Dushku
cs.CR · cs.AI
Edge AI accelerators are increasingly deployed in safety-critical environments, where model outputs may control physical actuators, make access-control decisions, or trigger alarms. In these settings, runtime failures often remain undetected because model corruption, distribution shift, and adversarial inputs can still produce well-formed, confident predictions. This paper presents TERMon, a lightweight hardware runtime monitor that detects...
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05
CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents
Tao Huang, Guosen Wu, Guolong Zheng, Jiayang Meng, Chen Hou, Xu Yang, Xuechao Yang, Feng Xia
cs.CR · cs.AI
Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval, or tool-use pipelines, which makes it difficult to distinguish internal exposure from information that an external observer can actually recover. We present CIPL (Channel Inversion for Privacy Leakage), a channel-aware evaluation framework for black-box privacy leakage in LLM agents. CIPL represents a target through sensitive source,...
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06
Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems
Pedro Pereira, Eva Maia, Isabel Praça
cs.CR · cs.AI
Retrieval-Augmented Generation (RAG) improves large language models by grounding outputs in external knowledge sources, but this dependency also creates a surface for poisoning attacks. This paper introduces Micro-Collaborative Poisoning, a distributed attack in which a false target claim is divided across multiple locally plausible documents instead of being concentrated in a single malicious passage. We evaluate the attack across 108 RAG...
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07
ServeGuard: Verifiable, Bounded-Residual Confinement of Operator-Invisible Channels Without Revealing the Certified Read Factor
Dominik Dahlem, Rui Vieira
cs.CR · cs.LG
Third-party adapters for open-weight language models ship as opaque weight matrices; a recipient cannot check whether an adapter hides a backdoor without trusting the publisher or inspecting the weights, the publisher's core asset. For one important class (payloads placed where a safety monitor is structurally blind), detection is unsound as a defense: every detector that factors through the declared monitor is invariant on its blind...
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08
HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference
Byeongseo Min, Yongwoo Lee, Young-Sik Kim, Yongjune Kim
cs.CR · cs.AI
Homomorphic encryption (HE) has emerged as a promising approach to privacy-preserving machine learning (PPML), enabling computation directly over encrypted data. In HE-based PPML, a client submits an encrypted input to the server, which evaluates models such as large language models (LLMs) without access to the underlying plaintext. However, we identify a critical security vulnerability in this setting: HE-LLM inference is vulnerable to...
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09
TrustBOM: A Scalable Architecture for Confidentiality-Preserving SBOMs Across Organizations
Van Thang Nguyen, Frederic Rupprecht, Tom Lawrence, Lucca Di Benedetto, Sören Schubert, Amor Rezgui, Sebastian...
cs.CR · cs.DC
Software Bills of Materials (SBOMs) have emerged as a key mechanism for software supply chain governance in enterprise architectures. However, their adoption across organizations remains limited due to concerns about exposing sensitive dependency information. To address this limitation, we propose TrustBOM, a scalable architecture for confidentiality-preserving SBOMs integrated into enterprise CI/CD workflows. TrustBOM enables software...
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10
CESBench: Benchmarking Large Language Models on Cryptographic Engineering Security for IoT Devices
Wenquan Zhou, An Wang, Jing Liang, Peien Feng, Jingqi Zhang, Yaoling Ding, Liehuang Zhu
cs.CR · cs.AI
For Internet of Things (IoT) devices, a secure algorithm alone is not enough: an attacker with physical access can attack the implementation directly, and its flaws are hard to fix once deployed. Large language models (LLMs) are now used to build and analyze such implementations. LLM benchmarks exist for cryptography and general cybersecurity, but none covers cryptographic engineering. In this paper, we present CESBench, 380 expert-written...
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11
Identifying Security Platform Product Abuse with Machine Learning
Shaefer Drew, Michael Brautbar, Paul Knight, Edward Raff, Lana Peric-McDermott, Simran Sarin, Nickolas Machado,...
cs.CR · cs.LG
Product abuse is an individually rare, but growing, problem across the SaaS industry. Highly sophisticated threat actors can misuse security platforms within customer environments or conduct bypass experiments on the product itself. Threat actors can leverage living-off-the-land (LOTL) attacks to avoid using cumbersome, frequently detected malware. Remediating this threat requires collecting multiple data modalities across different types of...
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