Satyajayant Misra

Pratyay Kumar, Miguel Antonio Guirao Aguilera, Srikathyayani Srikanteswara et al.

Model Context Protocol (MCP) servers have rapidly emerged over the past year as a widely adopted way to enable Large Language Model (LLM) agents to access dynamic, real-world tools. As MCP servers proliferate and become easy to adopt via open-source releases, understanding their security risks becomes essential for dependable production agent deployments. Recent work has developed MCP threat taxonomies, proposed mitigations, and demonstrated practical attacks. However, to the best of our knowledge, no prior study has conducted a systematic, large-scale assessment of weaknesses in open-source MCP servers. Motivated by this gap, we apply static code analysis to identify Common Weakness Enumeration (CWE) weaknesses and map them to common attack patterns and threat categories using the MITRE Common Attack Pattern Enumerations and Classifications (CAPEC) to ground risk in real-world threats. We then introduce a risk-assessment framework for the MCP landscape that combines these threats using a multi-metric scoring of likelihood and impact. Our findings show that many open-source MCP servers contain exploitable weaknesses that can compromise confidentiality, integrity, and availability, underscoring the need for secure-by-design MCP server development.

Abhinav Kumar, Miguel A. Guirao Aguilera, Reza Tourani et al.

The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical--a facet that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them unfit for real-time applications. We propose Fides, a novel framework for real-time integrity validation of ML-as-a-Service (MLaaS) inference. Fides features a novel and efficient distillation technique--Greedy Distillation Transfer Learning--that dynamically distills and fine-tunes a space and compute-efficient verification model for verifying the corresponding service model while running inside a trusted execution environment. Fides features a client-side attack detection model that uses statistical analysis and divergence measurements to identify, with a high likelihood, if the service model is under attack. Fides also offers a re-classification functionality that predicts the original class whenever an attack is identified. We devised a generative adversarial network framework for training the attack detection and re-classification models. The evaluation shows that Fides achieves an accuracy of up to 98% for attack detection and 94% for re-classification.

Abhinav Kumar, George Torres, Noah Guzinski et al.

The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless, these solutions have predominantly been crafted for and assessed in the context of synchronous FL systems, with minimal focus on asynchronous FL. This gap arises in part due to the unique challenges posed by the asynchronous setting, such as the lack of coordinated updates, increased variability in client participation, and the potential for more severe privacy risks. These concerns have stymied the adoption of asynchronous FL. In this work, we first demonstrate the privacy vulnerabilities of asynchronous FL through a novel data reconstruction attack that exploits gradient updates to recover sensitive client data. To address these vulnerabilities, we propose a privacy-preserving framework that combines a gradient obfuscation mechanism with Trusted Execution Environments (TEEs) for secure asynchronous FL aggregation at the network edge. To overcome the limitations of conventional enclave attestation, we introduce a novel data-centric attestation mechanism based on Multi-Authority Attribute-Based Encryption. This mechanism enables clients to implicitly verify TEE-based aggregation services, effectively handle on-demand client participation, and scale seamlessly with an increasing number of asynchronous connections. Our gradient obfuscation mechanism reduces the structural similarity index of data reconstruction by 85% and increases reconstruction error by 400%, while our framework improves attestation efficiency by lowering average latency by up to 1500% compared to RA-TLS, without additional overhead.

Milan Biswal, Abu Saleh Md Tayeen, Satyajayant Misra

Machine learning (ML) based smart meter data analytics is very promising for energy management and demand-response applications in the advanced metering infrastructure(AMI). A key challenge in developing distributed ML applications for AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes a privacy-preserving federated learning framework for ML applications in the AMI. We consider each smart meter as a federated edge device hosting an ML application that exchanges information with a central aggregator or a data concentrator, periodically. Instead of transferring the raw data sensed by the smart meters, the ML model weights are transferred to the aggregator to preserve privacy. The aggregator processes these parameters to devise a robust ML model that can be substituted at each edge device. We also discuss strategies to enhance privacy and improve communication efficiency while sharing the ML model parameters, suited for relatively slow network connections in the AMI. We demonstrate the proposed framework on a use case federated ML (FML) application that improves short-term load forecasting (STLF). We use a long short-term memory(LSTM) recurrent neural network (RNN) model for STLF. In our architecture, we assume that there is an aggregator connected to a group of smart meters. The aggregator uses the learned model gradients received from the federated smart meters to generate an aggregate, robust RNN model which improves the forecasting accuracy for individual and aggregated STLF. Our results indicate that with FML, forecasting accuracy is increased while preserving the data privacy of the end-users.

Emrah Sariboz, Kartick Kolachala, Gaurav Panwar et al.

We propose a novel framework for off-chain execution and verification of computationally-intensive smart contracts. Our framework is the first solution that avoids duplication of computing effort across multiple contractors, does not require trusted execution environments, supports computations that do not have deterministic results, and supports general-purpose computations written in a high-level language. Our experiments reveal that some intensive applications may require as much as 141 million gas, approximately 71x more than the current block gas limit for computation in Ethereum today, and can be avoided by utilizing the proposed framework.

Reza Tourani, Travis Mick, Satyajayant Misra et al.

Information-Centric Networking (ICN) is a new networking paradigm, which replaces the widely used host-centric networking paradigm in communication networks (e.g., Internet, mobile ad hoc networks) with an information-centric paradigm, which prioritizes the delivery of named content, oblivious of the contents origin. Content and client security are more intrinsic in the ICN paradigm versus the current host centric paradigm where they have been instrumented as an after thought. By design, the ICN paradigm inherently supports several security and privacy features, such as provenance and identity privacy, which are still not effectively available in the host-centric paradigm. However, given its nascency, the ICN paradigm has several open security and privacy concerns, some that existed in the old paradigm, and some new and unique. In this article, we survey the existing literature in security and privacy research sub-space in ICN. More specifically, we explore three broad areas: security threats, privacy risks, and access control enforcement mechanisms. We present the underlying principle of the existing works, discuss the drawbacks of the proposed approaches, and explore potential future research directions. In the broad area of security, we review attack scenarios, such as denial of service, cache pollution, and content poisoning. In the broad area of privacy, we discuss user privacy and anonymity, name and signature privacy, and content privacy. ICN's feature of ubiquitous caching introduces a major challenge for access control enforcement that requires special attention. In this broad area, we review existing access control mechanisms including encryption-based, attribute-based, session-based, and proxy re-encryption-based access control schemes. We conclude the survey with lessons learned and scope for future work.