Pradeep Yadlapalli | Searcharxiv

arXiv2026

Workspace Topology as an Attack Vector in Agentic Coding Assistants

Agentic coding assistants are finding widespread use, not just in new code development but in quickly ingesting and leveraging third-party code. This opens up a risk of malicious code being ingested as these coding tools operate with broad filesystem access inside developer workspaces. In this paper, we extensively study the impact of different dimensions of a novel attack surface we term workspace topology -- defined via directory depth, codebase modularity, in-file injection position and context framing -- on the attack success rate of adversarial prompt injection attempts. We perform an empirical study of indirect prompt injection (IPI) across a diverse set of open-source repositories spanning 10 languages and 6 engineering domains, evaluating three IPI entry points against open-weight models operating open source code harnesses. We find that workspace topology measurably affects IPI success. Specifically, changes in codebase modularity can significantly alter the Attack Success Rate (ASR), with highly modular environments demonstrating significantly lower attack success rates. Furthermore, context framing and introduction of security-cues in the workspace can also alter the ASR. Our findings offer practical value for the evaluation and security testing of coding agents across diverse settings, while underscoring the importance of an uncontaminated testing environment to obtain reliable results and conclusions.

cs.CR↗

arXiv2023

Advancement on Security Applications of Private Intersection Sum Protocol

Secure computation protocols combine inputs from involved parties to generate an output while keeping their inputs private. Private Set Intersection (PSI) is a secure computation protocol that allows two parties, who each hold a set of items, to learn the intersection of their sets without revealing anything else about the items. Private Intersection Sum (PIS) extends PSI when the two parties want to learn the cardinality of the intersection, as well as the sum of the associated integer values for each identifier in the intersection, but nothing more. Finally, Private Join and Compute (PJC) is a scalable extension of PIS protocol to help organizations work together with confidential data sets. The extensions proposed in this paper include: (a) extending PJC protocol to additional data columns and applying columnar aggregation based on supported homomorphic operations, (b) exploring Ring Learning with Errors (RLWE) homomorphic encryption schemes to apply arithmetic operations such as sum and sum of squares, (c) ensuring stronger security using mutual authentication of communicating parties using certificates, and (d) developing a Website to operationalize such a service offering. We applied our results to develop a Proof-of-Concept solution called JingBing, a voter list validation service that allows different states to register, acquire secure communication modules, install it, and then conduct authenticated peer-to-peer communication. We conclude our paper with directions for future research to make such a solution scalable for practical real-life scenarios.

cs.CR↗