Siva Kesava Reddy Kakarla

Rathin Singha, Kuan Qian, Srinath Saikrishnan et al.

Many software bugs in network protocol implementations arise near specification boundaries, such as inputs just within or outside allowed ranges, or messages that are valid in isolation but invalid in a given state. From the SSL Heartbleed exploit to TCP Christmas Tree packets, boundary inputs have repeatedly exposed critical weaknesses, yet remain under-tested by existing techniques such as fuzzing and model-based testing. We present CornerCase, an automated extremal testing approach that systematically targets such boundary behaviors. Our key idea is to decompose test generation into two stages: first, large language models (LLMs) extract explicit validity constraints from protocol specifications (e.g., RFCs) in a structured, section-by-section manner; second, extremal test cases are generated at or near the boundary of each constraint. These tests are executed across multiple implementations, and differential testing identifies inconsistencies. We evaluate CornerCase on widely used implementations of HTTP, DNS, BGP, SMTP, and QUIC, uncovering many previously unknown bugs. For example, the HTTP server h2o enters a redirect loop when processing URLs containing encoded null bytes. Overall, we used CornerCase to identify and file 42 anomalies; to date 26 have been acknowledged as bugs and 18 fixed, with others under active investigation

Pantea Karimi, Solal Pirelli, Siva Kesava Reddy Kakarla et al.

Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set), and they do not explain why. We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.

Pantea Karimi, Dany Rouhana, Pooria Namyar et al.

We posit that we can generate more robust and performant heuristics if we augment approaches using LLMs for heuristic design with tools that explain why heuristics underperform and suggestions about how to fix them. We find even simple ideas that (1) expose the LLM to instances where the heuristic underperforms; (2) explain why they occur; and (3) specialize design to regions in the input space, can produce more robust algorithms compared to existing techniques~ -- ~the heuristics we produce have a $\sim28\times$ better worst-case performance compared to FunSearch, improve average performance, and maintain the runtime.