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arXiv 最近完成扫描:2026-11-16 01:34 · 全站最近入库:2026-11-14 15:09各信源进度 →

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新论文 · v1arXiv:2611.15316

IoTPwnBench: A Synthetic Benchmark for IoT Firmware Security Analysis

Luca Ferrari, Marco Alecci, Luca Verderame, Jacques Klein, Francesco Pagano

原文摘要

Building and evaluating security tools for IoT firmware requires targets whose weaknesses are known in ad- vance and can be reproduced in a running device. Two problems remain: (1) firmware collected from real-world products provides realistic software, but hardware dependencies and undocumented defects make it difficult to establish what a security tool should be expected to find; and (2) existing benchmarks typically collect sets of vulnerable or misconfigured firmware images without providing purpose-built vulnerable services with explicit weaknesses that a security analysis tool can target. We present IoTPwnBench, a synthetic, modular benchmark that brings these requirements together. IoTPwnBench contains 716 vulnerable firmware images covering 86 weakness scenarios and 28 CWE classes across 11 device profiles, including industrial controllers, health monitors, and household devices. Each vulnerable image runs a plausible device application with simulated sensors and actuators that generate live readings over its native interfaces while exposing one target weakness mapped to a primary CWE. A documented exploitation payload and observable signal establish the expected behavior independently of the evaluated tool. Every vulnerable image is paired with a corrected twin that preserves the device and service while removing that weakness. Researchers can therefore compare the same target before and after correction to assess detection and false positives for the target weakness. The benchmark produces kernel and filesystem binaries for static inspection and dynamic testing under QEMU. Device profiles and weakness modules remain separate, allowing the same scenario to be reused across device contexts. Vulnerable targets were validated manually, and the open source build workflow regenerates the firmware bit-for-bit.

cs.CR · cs.SE

新论文 · v1arXiv:2611.13125

MiniZoo: An Evolving Dataset of Mini Apps for the Research Community

Luca Ferrari, Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé, Jacques Klein, Luca Verderame

原文摘要

Mini apps run within host applications (apps) such as WeChat, Alipay, Baidu, and Telegram, providing services without requiring users to install separate native apps. Their growing ecosystem has attracted research on software quality, security, and privacy, creating a need for reusable datasets. However, building and maintaining such datasets is challenging, requiring platform-specific collection infrastructure and mechanisms to handle incomplete packages, changing availability, and heterogeneous metadata. A shared, continuously maintained collection is still lacking, therefore motivating the development of a common research resource, whose value has been demonstrated by the experience of AndroZoo in the Android ecosystem. In this paper, we present MiniZoo, an evolving corpus of mini apps and associated metadata. The initial release comprises 16,273 mini-app packages, with multiple versions of the same mini apps. The dataset is managed and distributed through AndroZoo under its existing access conditions. To facilitate its reuse, we describe the collection process, data organization, provenance, and limitations, and explain how researchers can select and access the data. MiniZoo is designed to grow continuously through the addition of newly discovered mini apps, the inclusion of further host ecosystems, and the collection of successive versions of previously collected apps. This ongoing expansion will enable both comparisons across ecosystems and longitudinal studies of how mini apps evolve over time.

cs.SE

新论文 · v1arXiv:2611.07887

A Low Similarity Score Does Not Show Independence after Source-Conditioned Repository Rewriting

Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Tailia Malloy, Abdoul Kader Kaboré, Jacques Klein, Tegawendé F. Bissyande

原文摘要

Maintainers sometimes cite a low token-similarity score as evidence that an LLM-assisted rewrite of a copyleft project is independent of the original. We test what such a score can support under \emph{source-conditioned} rewriting: a coding agent keeps the original in its working copy, rewrites the repository, and iterates until a fixed behavioural harness passes, with no human edit to the code. On 69 rewrites of six copyleft Python projects we find that: \ding{182}~Agent-only rewriting is inexpensive and often yields low scores. Every controlled rewrite passes the harness after repair, usually within half an hour for the initial rewrite. Under a ground-up rearchitecture prompt, most rewrites fall below 25\% JPlag similarity and still pass the harness. \ding{183}~A single low score is an unreliable witness. Identical project--agent--prompt configurations produce wide spreads in similarity; run-to-run variation dominates project and agent effects, and in more than a third of configurations the three repeats straddle the threshold. Rewrites retain more of the original's imports and fingerprint facts than unrelated projects do, yet hand-written independent implementations of the same task often retain as much, and neither signal separates the two groups better than chance. Thus a low score on one rewrite is weak evidence of independence: another run of the same agent may score high. Claims based on similarity should report repeated generations, and candidate provenance signals should be checked against same task independent implementations, not only against unrelated projects.

cs.SE

版本更新 · v3arXiv:2502.13828

Software Security in Software-Defined Networking: A Systematic Literature Review

Moustapha Awwalou Diouf, Samuel Ouya, Jacques Klein, Tegawendé F. Bissyandé

原文摘要

Software-defined networking (SDN) separates the control plane from the data plane and exposes the network through software applications and open APIs. The same programmability that drove its adoption also turned the network into a large body of software, and that software can be vulnerable. Knowing where and how SDN software fails is a prerequisite for defending it, yet the literature offers no consolidated account of the problem. We address this gap with a systematic literature review of 113 primary studies published between 2012 and 2025 on the security of the software that makes up SDN. We study how each software component becomes vulnerable, how attackers exploit it, which testing and analysis techniques expose its defects, and how the field has evolved. The review yields a taxonomy of vulnerabilities and attack vectors organized by SDN software component, a synthesis of the methods used to find software defects in each component, and a set of open problems that mark the most promising directions for future work. Earlier surveys treat SDN security as a networking problem; ours is, to our knowledge, the first to treat SDN components as software artifacts whose code, logic, and interactions can be defective. Our artifacts are available at https://github.com/mad975/SDNSoftwareSecurity_SLR.

cs.CR · cs.SE

新论文 · v1arXiv:2610.39489

Program Bindings Inside Code Models

Prateek Kumar Rajput, Yewei Song, Jacques Klein, Tegawendé F. Bissyandé

原文摘要

Code language models complete, review, and repair programs without exposing the program structure encoded in their hidden states. In this paper, we test whether the hidden states of a code model can be described and edited using the information a compiler front end computes. For each token of a Python program, standard analyzers provide its position in the abstract syntax tree, its scope, its definition-use links, and its syntactic category. We fit a decomposition that expresses every hidden state of a frozen model as a sum of one term per token, each combining the token with this structural label, and we replace the model's own states with the decomposition at every layer while it predicts the output of a small program. If the model still answers correctly, the decomposition preserves the information the model uses. We apply this test to 14 open models, seven of them trained for code, and compare against a baseline decomposition of the same size built from token positions. The static-analysis decomposition preserves the model's answers better than the positional one in 10 of the 14 models, including six of the seven code models, and deleting the definition-use links from it removes most of that advantage on the two models tested. Editing the decomposition, for instance rebinding the argument of a call, makes StarCoder2-15B produce the answer of the edited program in 90 of 100 cases, compared with 19 for the positional baseline. The Qwen2.5 family is the exception on several measures. For most of the models tested, standard static analysis therefore provides both a description of what the hidden states encode about a program and a way to change it without retraining.

cs.SE