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Pubali Datta

Publications and source records attributed to Pubali Datta.

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Permissions on the Loose: Measuring Overprivilege in Real-World Serverless Applications

Serverless computing has seen rapid adoption in cloud deployments, yet the security implications of its service-oriented programming model remain poorly understood. Distributed, modular, and heterogeneous applications complicate the specification of precise security policies. Role-based access control solutions such as Identity and Access Management (IAM) already exhibit pervasive misconfiguration problems, and the multiplicity of functions, services, and resources in serverless applications, together with frequent permission model changes by cloud providers, greatly increases the likelihood of policy misconfigurations. Consequently, policies are often overprivileged, thereby enlarging the attack surface and exposing sensitive cloud resources to compromise. We present a large-scale measurement study of overprivilege in real-world serverless applications, analyzing a curated dataset of 789 AWS Lambda applications comprised of 1,293 functions. To enable this study, we develop PrivLess, a static policy analysis framework that extracts function-to-resource interactions from application source code, derives an interaction-permission mapping, and reconciles inferred interactions with declared policies to quantify overprivilege. Our measurement reveals that overprivilege is systemic and severe across the serverless ecosystem: 47.7% of applications carry excess permissions with a significant privilege reduction potential of 99.65%. Applications with wildcard-defined permissions exhibited an average overprivilege ratio 274x higher than those without. More critically, the excess permissions enable concrete attack vectors: 18.8% of applications hold unnecessary Privilege Escalation capabilities, and 12 applications had Defense Evasion permissions they did not need.

cs.CR

Whose Agent Are You? Multi-Layer Fingerprinting and Attribution of Autonomous Web Agents

As AI web agents proliferate, combining large language models with autonomous, browser-level control, indiscriminate content scraping by web agents has emerged as a privacy and security challenge. Existing defenses, such as robots.txt and active bot-blocking, are insufficient, as they are widely violated and easily circumvented. In this work, we demonstrate that AI web agents can be effectively distinguished from humans and traditional crawlers using a multi-layer fingerprint based on both network layer characteristics (e.g., TLS, HTTP) and browser interaction behavior. We implement this mechanism as a programmatic logging framework that can be deployed on a live, instrumented domain. By analyzing six prominent agent frameworks (AutoGen, Browser Use, Claude, Gemini, Operator, and Skyvern), we uncover latent structural differences in how these systems assemble HTTP requests, establish TLS/HTTP connections, and execute autonomous browser actions. Feeding these multi-layer features into a decision tree classifier, our framework achieves high-fidelity identification (97% accuracy), successfully isolating distinct agent architectures and differentiating agent traffic from both human browsing baselines and legacy crawlers. Our findings demonstrate that cross-layer agent tracking provides a robust, evasion-resistant strategy for content protection and web security policy enforcement.

cs.CR

CensorLess: Cost-Efficient Censorship Circumvention Through Serverless Cloud Functions

With the increase in Internet censorship globally, various circumvention tools have been designed and developed. However, the monetary cost of these tools deeply impacts both user choice and the sustainability of provider operations. Recent developments in censorship circumvention research attempted to achieve cost efficiency by utilizing Infrastructure-as-a-Service (IaaS) spot instances as bridges, but still incurred substantial expenses related to network connectivity and instance maintenance. In this work, we present CensorLess, a circumvention proxy built leveraging the unique benefits of a serverless platform. CensorLess comprises three components: a local proxy that handles client-side communication and ensures compliance with serverless functions' security restrictions, a function refresher that periodically regenerates bridges, and a live migration mechanism that maintains continuous connectivity. CensorLess inherits the serverless platform's cost efficiency, ephemerality, scalability, concurrency, and performance. Compared to existing low-cost, state-of-the-art circumvention techniques, CensorLess reduces costs by 97%, while simultaneously enabling robust censorship resistance by employing bridge rotation.

cs.CR

International Students and Scams: At Risk Abroad

International students (IntlS) in the US refer to foreign students who acquire student visas to study in the US, primarily in higher education. As IntlS arrive in the US, they face several challenges, such as adjusting to a new country and culture, securing housing remotely, and arranging finances for tuition and personal expenses. These experiences, coupled with recent events such as visa revocations and the cessation of new visas, compound IntlS' risk of being targeted by and falling victim to online scams. While prior work has investigated IntlS' security and privacy, as well as general end users' reactions to online scams, research on how IntlS are uniquely impacted by scams remains largely absent. To address this gap, we conduct a two-phase user study comprising surveys (n=48) and semi-structured interviews (n=9). We investigate IntlS' exposure and interactions with scams, post-exposure actions such as reporting, and their perceptions of the usefulness of existing prevention resources and the barriers to following prevention advice. We find that IntlS are often targeted by scams (e.g., attackers impersonating government officials) and fear legal implications or deportation, which directly impacts their interactions with scams (e.g., they may prolong engagement with a scammer due to a sense of urgency). Interestingly, we also find that IntlS may lack awareness of - or access to - reliable resources that inform them about scams or guide them in reporting incidents to authorities. In fact, they may also face unique barriers in enacting scam prevention advice, such as avoiding reporting financial losses, since IntlS are required to demonstrate financial ability to stay in the US. The findings produced by our study help synthesize guidelines for stakeholders to better aid IntlS in reacting to scams.

cs.CR