cs.CR · 2026-07-29 · No. 68

Cryptography and Security, 2026-07-29.

7 new papers in cs.CR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

7 entries
  1. 01

    Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?

    Farooq Shaikh

    cs.CR · cs.AI

    Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can automate cluster security remediation, generating configuration patches from Kubernetes Security Posture Management (KSPM) findings without human authoring. Such systems, however, prompt the model with each finding in isolation from the live service call graph, assuming general hardening...

    arxiv.org/abs/2607.25995 · PDF

  2. 02

    Stemma: Induced Decision Regions Reveal LLM Provenance

    Keyu Zhang, Vadim Safronov, Andrew Martin

    cs.CR · cs.AI · cs.CL

    LLM provenance testing asks whether a suspect LLM belongs to the same lineage as a source. Existing black-box methods largely infer this relationship from response-level characteristics, but these characteristics may shift under adaptation or deployment even when the underlying meaning remains unchanged, weakening the reliability of provenance evidence. To address this limitation, we introduce induced decision regions by mapping open-ended...

    arxiv.org/abs/2607.25880 · PDF

  3. 03

    Optimistic Verifiable Claims: A Blockchain Protocol for Conditionally Confidential Bidding in Decentralized Manufacturing

    Marko Corn, Nejc Rožman, Primož Podržaj

    cs.CR · cs.DC · cs.GT

    Decentralized manufacturing faces a pre-contractual impasse: a Provider cannot price a service accurately without inspecting the design file, yet the Consumer cannot share that file without exposing intellectual property. We introduce the Optimistic Verifiable Claim (OVC), a blockchain protocol that lets a Consumer publish a verifiable claim about a concealed design (such as the material it consumes) and a Provider price and bid on it without...

    arxiv.org/abs/2607.25517 · PDF

  4. 04

    Architectural Backdoors in Vision-Language Model Supply Chains via Representation Steering

    Maria Rosaria Briglia, Igor Maljkovic, Antonio Emanuele Cinà, Luca Oneto, Iacopo Masi, Fabio Roli

    cs.CR · cs.AI · cs.LG

    Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text encoders, and exported computation graphs are distributed by third parties and reused across downstream services. This reuse model creates a security-critical trust boundary: VLM deployments inherit not only learned parameters but also executable behavior encoded in shared model artifacts. In...

    arxiv.org/abs/2607.25479 · PDF

  5. 05

    Hybrid Analysis for Secure MCP Tool Use in LLM Agents

    Ping He, Yuexiang Xie, Yaliang Li, Shouling Ji

    cs.CR · cs.AI

    The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks. To standardize interactions between LLM agents and external environments, Model Context Protocol (MCP) tools have emerged as a de facto standard and have been widely integrated into these systems. However, the use of MCP tools also introduces new safety risks, as LLM agents can be induced to perform malicious or...

    arxiv.org/abs/2607.25297 · PDF

  6. 06

    Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks

    Yu Yan, Jiahao Chen, Siqi Lu, Yongjuan Wang, Ziming Zhao, Zhaoxuan Li, Tianyu Du, Qingjun Yuan, Shouling Ji

    cs.CR · cs.LG

    Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly relying on their outputs. However, the deep integration of open-source model sharing ecosystems with LLM-powered critical decision-making applications also introduces critical risks: if an attacker can manipulate the model's cognitive stance, they can indirectly influence the judgments and...

    arxiv.org/abs/2607.25227 · PDF

  7. 07

    SecDrift: Measuring Sector-Conditioned Security Drift in AI-Generated Code

    Narayanaswami Natraj Bharadwaj, Dhivya Chandramouleeswaran

    cs.CR · cs.LG · cs.SE

    LLMs are increasingly used for code generation in critical infrastructure, yet the security effect of domain-specific prompting is understudied. We present SecDrift, a benchmark measuring sector-conditioned security drift: the change in static-analysis vulnerability rates when prompts are conditioned on industry contexts versus neutral baselines. We evaluate 7 LLMs (6 producing analyzable code) across 8 CISA critical infrastructure sectors...

    arxiv.org/abs/2607.25225 · PDF

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