sandboxing.html 关键词 sandboxing 的长期追踪 RSS，汇总历史命中文献。 zh-CN Sun, 28 Jun 2026 05:24:06 +0000 ../papers/arxiv-471450e505c8.html https://arxiv.org/abs/2606.24778v1#2026-06-24#sandboxing Wed, 24 Jun 2026 13:06:49 +0800 Malware analysis is a critical aspect of modern cybersecurity. The prevailing industry practice, sandboxing, involves executing suspicious binaries within isolated virtual machines in large-scale data centers. However, this approach can unintentionally expose samples to public platforms such as VirusTotal and MalwareBazaar, and it is both resource-intensive and time-consuming. Burnyard addresses these limitations through a lightweight binary emulation platform that captures observable runtime b… ../papers/arxiv-199c63f3e69d.html https://arxiv.org/abs/2606.19992v1#2026-06-19#sandboxing Fri, 19 Jun 2026 14:26:15 +0800 In the agentic web era, LLM-based agents increasingly invoke web services as tools, yet most interfaces remain \emph{static endpoints} that poorly express long-horizon workflows with loops, conditionals, joins, and retries. We present ToolPro, which represents an agent's tool intent as an \emph{executable tool program} that compactly encodes multi-step service interactions with explicit effect types. ToolPro combines constraint-guided program construction, effect-aware replay for exactly-once s… ../papers/arxiv-80675a0579e2.html https://arxiv.org/abs/2606.10615v1#2026-06-10#sandboxing Wed, 10 Jun 2026 13:25:04 +0800 Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme computational cost. We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that p… ../papers/arxiv-cb264e187530.html https://arxiv.org/abs/2605.10763v1#2026-05-12#sandboxing Tue, 12 May 2026 12:42:08 +0800 LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. We present MATRA, a pragmatic threat modeling framework for agentic AI systems that adapts established risk assessment methodology to systematically assess how known LLM threats translate into deployment-specific risks.…