Rakesh Podder

Rakesh Podder, Wadia Ganim, Sarath Sreedharan et al.

i-EXAM is a planning-powered tool that helps system administrators to create security profiles of complex networks and perform what-if analyses to identify network hardening strategies. It leverages planning compilation that provides soundness and completeness guarantees to identify attack paths, evaluate security metrics, generate diverse hardening strategies, and explain these strategies in natural language using Large Language Models.

Rakesh Podder, Rafael Fabian Gonzalez Arellano, Indrajit Ray

Modern software supply chains comprise hundreds of transitive dependencies, yet existing analysis tools operate at either the ecosystem level (dependency graphs) or the code level (static analysis within packages). This separation creates two failure modes. First, false-positive CVE alerts for unreachable code. Second, blind spots for structurally critical micro-dependencies. We introduce cross-level risk propagation, a framework that bridges code-level risk metrics with ecosystem-level dependency exposure through a unified risk formula. Preliminary evaluation on 50 packages across npm and PyPI reveals a class of hidden amplifiers -- micro-dependencies with fewer than 50 methods but over 50,000 dependents -- that carry outsized supply-chain risk invisible to all current Software Composition Analysis (SCA) tools. Without cross-level analysis, such packages can harbor exploitable code for years because no current tool considers both internal code structure and ecosystem position simultaneously. These results suggest that cross-level analysis opens a new design space for supply-chain security.

Rakesh Podder, Turgay Caglar, Shadaab Kawnain Bashir et al.

Graph-based frameworks are often used in network hardening to help a cyber defender understand how a network can be attacked and how the best defenses can be deployed. However, incorporating network connectivity parameters in the attack graph, reasoning about the attack graph when we do not have access to complete information, providing system administrator suggestions in an understandable format, and allowing them to do what-if analysis on various scenarios and attacker motives is still missing. We fill this gap by presenting SPEAR, a formal framework with tool support for security posture evaluation and analysis that keeps human-in-the-loop. SPEAR uses the causal formalism of AI planning to model vulnerabilities and configurations in a networked system. It automatically converts network configurations and vulnerability descriptions into planning models expressed in the Planning Domain Definition Language (PDDL). SPEAR identifies a set of diverse security hardening strategies that can be presented in a manner understandable to the domain expert. These allow the administrator to explore the network hardening solution space in a systematic fashion and help evaluate the impact and compare the different solutions.