博士研究生岗位 F/M 检测危险浏览器扩展，以保护网络用户的安全与隐私

该博士项目旨在解决危险浏览器扩展所带来的安全与隐私风险问题。其目标是通过利用机器学习和分析技术，开发自动化工具以识别不同浏览器中的恶意行为。申请者应具备扎实的计算机科学基础，并对网络安全与网络隐私领域有浓厚兴趣。

Assignment: Context on Browser Extension Security Given the central role of the Web in our daily lives and its overall impact on society and economy, it is crucial that Web pages and browser extensions assure the security and privacy of their large user base. Browser extensions have become an integral part of the modern Web ecosystem. The most popular desktop browser Chrome lists 280k browser extensions [1] (totaling almost 3 billion active users) in its official gallery, the Chrome Web Store [ 2 ]. Browser extensions enable users to enhance and customize their browsing experience: typically, extensions offer additional functionality to Web users, such as ad blocking (e.g., AdBlock), grammar checks (e.g., Grammarly), or password management (e.g., LastPass). To operate, browser extensions need elevated privileges compared to Web pages. Browser extensions can access sensitive user data, such as cookies or browsing history, and they can interact with arbitrary websites and other extensions [3, 4, 5]. While a compromised or malicious Web page is in itself troublesome, a malicious or vulnerable browser extension is much more powerful, as an attacker can leverage an extension's elevated privileges to cause more harm [3]. In particular, browser extensions can exfiltrate sensitive user data [6], track users online [7], or propagate unwanted software [8]. Detecting such threats at scale remains a challenge, as the extension ecosystem is particularly large, diverse, and continuously evolving. Limitations of existing work Abundant prior work attempted to detect some classes of dangerous extensions [9], e.g., malicious [10], vulnerable [3], or fingerprintable extensions [11]. However, related works have major limitations. Existing works analyze only hundreds of extensions [12], are outdated (e.g., 2012–2015 [8, 13, 14, 15]), have limited scalability and necessitate significant manual effort [16, 4], lack a clear characterization of what a dangerous behavior is [14]; and finally, only a small minority thereof is open source, as we have highlighted in [1 0 ]. Besides, existing machine learning-based detectors are opaque, they lack interpretability and explainability. It is therefore critical to design automated, large-scale, and reliable security and privacy analyses of browser extensions that have the potential to be deployed in the real world. References : [1] chrome-stats. Chrome extension statistics. ［会员资料链接］ [2] Google. Chrome Web Store. ［会员资料链接］ [3] ［会员专属信息］ ［会员专属信息］ , Dolière Francis Somé, Michael Backes, and Ben Stock. DoubleX: Statically Detecting Vulnerable Data Flows in Browser Extensions at Scale. In ACM CCS, 2021. Code repository: ［会员资料链接］ [4] Dolière Francis Somé. Empoweb: Empowering web applications with browser extensions. In IEEE S&P, 2019. [5] Jianjia Yu, Song Li, Junmin Zhu, and Yinzhi Cao. Coco: Efficient browser extension vulnerability detection via coverage-guided, con- current abstract interpretation. In ACM CCS, 2023. [6] Quan Chen and Alexandros Kapravelos. Mystique: Uncovering information leakage from browser extensions. In ACM CCS, 2018. [7] Michael Weissbacher, Enrico Mariconti, Guillermo Suarez-Tangil, Gianluca Stringhini, William Robertson, and Engin Kirda. Ex-ray: Detection of history-leaking browser extensions. In ACSAC, 2017. [8] Xinyu Xing, Wei Meng, Byoungyoung Lee, Udi Weinsberg, Anmol Sheth, Roberto Perdisci, and Wenke Lee. Understanding malvertising through ad-injecting browser extensions. In TheWebConf, 2015. [9] Sheryl Hsu, Manda Tran, and ［会员专属信息］ ［会员专属信息］ . What is in the Chrome Web Store? Investigating Security-Noteworthy Browser Extensions. In ACM AsiaCCS, 2024. [10] Ben Rosenzweig, Valentino Dalla Valle, Giovanni Apruzzese, and ［会员专属信息］ ［会员专属信息］ . It's not easy: Applying supervised machine learning to detect malicious extensions in the chrome web store. In ACM Transactions on the Web (TWEB), 2026. Code repository: ［会员资料链接］ [11] Shubham Agarwal, ［会员专属信息］ ［会员专属信息］ , and Ben Stock. Peeking through the window: Fingerprinting browser extensions through page-visible execution traces and interactions. In ACM CCS, 2024. Code repository: ［会员资料链接］ [12] Nicholas Carlini, Adrienne Porter Felt, and David Wagner. An evaluation of the google chrome extension security architecture. In USENIX Security, 2012. [13] Nav Jagpal, Eric Dingle, Jean-Philippe Gravel, Panayiotis Mavrommatis, Niels Provos, Moheeb Abu Rajab, and Kurt Thomas. Trends and lessons from three years fighting malicious extensions. In USENIX Security, 2015. [14] Alexandros Kapravelos, Chris Grier, Neha Chachra, Christopher Kruegel, Giovanni Vigna, and Vern Paxson. Hulk: Eliciting malicious behavior in browser extensions. In USENIX Security, 2014. [15] Kurt Thomas, Elie Bursztein, Chris Grier, Grant Ho, Nav Jagpal, Alexandros Kapravelos, Damon Mccoy, Antonio Nappa, Vern Paxson, Paul Pearce, Niels Provos, and Moheeb Abu Rajab. Ad injection at scale: Assessing deceptive advertisement modifications. In IEEE S&P, 2015. [16] Nikolaos Pantelaios, Nick Nikiforakis, and Alexandros Kapravelos. You’ve changed: Detecting malicious browser extensions through their update deltas. In ACM CCS, 2020. Main activities: Objectives of the PhD The broad objective of this PhD is to develop new automated tools to detect dangerous browser extensions , with a specific focus on malicious extensions. While the majority of prior work focuses on Chrome, we also want to study dangerous extensions on other browsers, such as Firefox and Edge, since we showed that the same malicious extensions can be published on multiple galleries [1]. - Objective 1 : Generate a large-scale dataset of browser extensions. Establish a systematic and comprehensive taxonomy of dangerous browser extension behaviors. - Objective 2 : Design novel, automated, and explainable methods to detect dangerous browser extensions, combining JavaScript static and lightweight dynamic analyses, predictive machine learning, and Large-Language Models. Provide interpretable and continuously adaptable detection models. - Objective 3 (in collaboration with a postdoctoral researcher, to be recruited): Assess how different stakeholders (i.e., platform maintainers, developers, and users) understand and manage the underlying security and privacy risks of browser extensions. Keywords : Web Security & Privacy, Browser Extensions, Dangerous Behavior Detection, Code Analysis, Applied Machine Learning, (Human Factors). [1] Abdullah Hassan Chaudhry, Valentino Dalla Valle, and ［会员专属信息］ ［会员专属信息］ . Two Heads are Better Than One: Analysing Browser Extensions Across Stores. In MADWeb (workshop co-located with NDSS), 2026. Code repository: ［会员资料链接］

Skills: Skills : A successful candidate for a PhD: should have a strong background in Computer Science ( Master degree or equivalent); with a major ideally in Cybersecurity ; their research interests should be in Web Security, Web Privacy, or Web Measurements . We are looking for a motivated student with: solid programming skills in python ; (at least) a basic background in Web Security & Privacy ; students with prior knowledge with Web-development technology (e.g., JavaScript), browser extensions, and browser architecture will be given priority; some basic knowledge of Machine Learning is a plus; proficiency in spoken and written English is a must ( C1 level or higher); French knowledge is not necessary (note that English is the default language in the team); we expect the student to be curious, creative, and have a strong willingness to learn and improve . The keys to success: How to apply : If you are interested in this PhD position, please apply by November 2, 2026 (11:59pm CET). You should provide: An up-to-date CV ; A cover letter highlighting why you are applying for this position, why you would be a good fit for the research team and why you wish to join Inria. Generic application materials that do not demonstrate a genuine interest in the position (e.g., materials generated primarily using an LLM) will be ignored; All available BSc and MSc transcripts , including, if available, your class ranking and/or ranking within your chosen specialization. International applicants are encouraged to include the grading scale, if available; If you are currently pursuing your MSc degree : a scientific description of your ongoing or planned research internship , if applicable. Otherwise, if you have completed your MSc degree : a copy of your Master's degree diploma and a summary of your MSc thesis (the thesis itself is not required); Optionally: letters of recommendation or other documents that support the quality of your application. All documents must be submitted in PDF format . Applications containing documents in other formats will not be considered. We strongly encourage applicants to submit their application materials in English , as all interviews will be conducted in English.

福利包： 补贴餐食 部分公共交通费用报销 休假规定：每年7周年假 + 因RTT（法定工作时间减少）额外享有10天假期 + 可能享受特殊假期（如照顾生病的子女、搬家等）。 远程工作及灵活工作时间安排的可能性 提供专业设备（视频会议、计算机设备借用等） 社会、文化和体育活动及项目 职业培训机会 雇主提供的健康保险计划 薪酬： 每月税前薪资：2300欧元 36个月固定期限合同 申请该职位

Instruction to apply: Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.