Behnaz Hassanshahi

Erik Pautsch, Tanmay Singla, Wenxin Jiang et al.

LLM-based agents are rapidly proliferating, yet the infrastructure for discovering, evaluating, and governing them remains fragmented compared to mature ecosystems like software package registries (e.g., npm) and model hubs (e.g., Hugging Face). Recent research and engineering works have begun to consider the requisite infrastructure, but so far they focus narrowly -- on distribution, naming, or protocol negotiation. However, considering broader software engineering requirements would improve open-source distribution and ease reuse. We therefore propose AgentHub, a research agenda for agent sharing. By framing the key challenges of capability clarity, lifecycle transparency, interoperability, governance, security, and workflow integration, AgentHub charts a community-wide agenda for building reliable and scalable agent ecosystems. Our vision is a future where agents can be shared, trusted, and composed as seamlessly as today's software libraries.

François Gauthier, Behnaz Hassanshahi, Benjamin Selwyn-Smith et al.

Following the advent of the American Fuzzy Lop (AFL), fuzzing had a surge in popularity, and modern day fuzzers range from simple blackbox random input generators to complex whitebox concolic frameworks that are capable of deep program introspection. Web application fuzzers, however, did not benefit from the tremendous advancements in fuzzing for binary programs and remain largely blackbox in nature. This paper introduces BackREST, a fully automated, model-based, coverage- and taint-driven fuzzer that uses its feedback loops to find more critical vulnerabilities, faster (speedups between 7.4x and 25.9x). To model the server-side of web applications, BackREST automatically infers REST specifications through directed state-aware crawling. Comparing BackREST against three other web fuzzers on five large (>500 KLOC) Node.js applications shows how it consistently achieves comparable coverage while reporting more vulnerabilities than state-of-the-art. Finally, using BackREST, we uncovered nine 0-days, out of which six were not reported by any other fuzzer. All the 0-days have been disclosed and most are now public, including two in the highly popular Sequelize and Mongodb libraries.

Mazharul Islam, Sazzadur Rahaman, Na Meng et al.

Spring security is tremendously popular among practitioners for its ease of use to secure enterprise applications. In this paper, we study the application framework misconfiguration vulnerabilities in the light of Spring security, which is relatively understudied in the existing literature. Towards that goal, we identify 6 types of security anti-patterns and 4 insecure vulnerable defaults by conducting a measurement-based approach on 28 Spring applications. Our analysis shows that security risks associated with the identified security anti-patterns and insecure defaults can leave the enterprise application vulnerable to a wide range of high-risk attacks. To prevent these high-risk attacks, we also provide recommendations for practitioners. Consequently, our study has contributed one update to the official Spring security documentation while other security issues identified in this study are being considered for future major releases by Spring security community.

Behnaz Hassanshahi, Hyunjun Lee, Paddy Krishnan et al.

Even though a lot of effort has been invested in analyzing client-side web applications during the past decade, the existing tools often fail to deal with the complexity of modern JavaScript applications. However, from an attacker point of view, the client side of such web applications can reveal invaluable information about the server side. In this paper, first we study the existing tools and enumerate the most crucial features a security-aware client-side analysis should be supporting. Next, we propose GELATO to detect vulnerabilities in modern client-side JavaScript applications that are built upon complex libraries and frameworks. In particular, we take the first step in closing the gap between state-aware crawling and client-side security analysis by proposing a feedback-driven security-aware guided crawler that is able to analyze complex frameworks automatically, and increase the coverage of security-sensitive parts of the program efficiently. Moreover, we propose a new lightweight client-side taint analysis that outperforms the start-of-the-art tools, requires no modification to browsers, and reports non-trivial taint flows on modern JavaScript applications.

Alexander Jordan, François Gauthier, Behnaz Hassanshahi et al.

The popularity of the PDF format and the rich JavaScript environment that PDF viewers offer make PDF documents an attractive attack vector for malware developers. PDF documents present a serious threat to the security of organizations because most users are unsuspecting of them and thus likely to open documents from untrusted sources. We propose to identify malicious PDFs by using conservative abstract interpretation to statically reason about the behavior of the embedded JavaScript code. Currently, state-of-the-art tools either: (1) statically identify PDF malware based on structural similarity to known malicious samples; or (2) dynamically execute the code to detect malicious behavior. These two approaches are subject to evasion attacks that mimic the structure of benign documents or do not exhibit their malicious behavior when being analyzed dynamically. In contrast, abstract interpretation is oblivious to both types of evasions. A comparison with two state-of-the-art PDF malware detection tools shows that our conservative abstract interpretation approach achieves similar accuracy, while being more resilient to evasion attacks.