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Home/Authors/Amin Milani Fard

Amin Milani Fard

5 indexed papers

Recent (6 mo)
5
With code
0
Influential cites
0
Benchmarked
0

Publications per year

5
26

Top categories

Crypto×5Software Eng.×3

Frequent co-authors

Charoes Huang3×
Xin Huang3×
Jayson Ng1×
Zhijun Jiang1×
Ngoc Phu Tran1×

Research Timeline

2026
Model Context Protocol Threat Modeling and Analyzing Vulnerabilities to Prompt Injection with Tool Poisoning

This paper analyzes the security vulnerabilities of the Model Context Protocol (MCP), identifying tool poisoning as the most critical client-side threat, and proposes a multi-layered defense strategy.

Are AI-assisted Development Tools Immune to Prompt Injection?

The paper empirically analyzes the susceptibility of seven widely used AI-assisted development tools (MCP clients) to prompt injection via tool-poisoning, revealing significant disparities in their security guardrails.

Auditing MCP Servers for Over-Privileged Tool Capabilities

The paper introduces mcp-sec-audit, a comprehensive toolkit that assesses Model Context Protocol (MCP) servers for over-privileged and insecure tool capabilities.

SPARK: Secure Predictive Autoscaling for Robust Kubernetes

SPARK introduces a predictive, traffic-aware autoscaling toolchain for Kubernetes that uses eBPF to enhance security and significantly reduce timeout errors during sudden traffic spikes.

Evaluating Retrieval-Augmented Generation for Explainable Malware Analysis

This paper empirically evaluates the use of Retrieval-Augmented Generation (RAG) for malware explanation and finds that RAG frequently degrades explanation quality by adding noise when structured security evidence is already available.

Highlighted terms show continued research focus across papers

Papers

cs.CRRecentMay 4, 2026

Evaluating Retrieval-Augmented Generation for Explainable Malware Analysis

Jayson Ng, Amin Milani Fard

This paper empirically evaluates the use of Retrieval-Augmented Generation (RAG) for malware explanation and finds that RAG frequently degrades explanation quality by adding noise when structured secu…

View →
cs.CRRecentMar 27, 2026

SPARK: Secure Predictive Autoscaling for Robust Kubernetes

Zhijun Jiang, Amin Milani Fard

SPARK introduces a predictive, traffic-aware autoscaling toolchain for Kubernetes that uses eBPF to enhance security and significantly reduce timeout errors during sudden traffic spikes.

View →
cs.CRcs.SERecentMar 23, 2026

Model Context Protocol Threat Modeling and Analyzing Vulnerabilities to Prompt Injection with Tool Poisoning

Charoes Huang, Xin Huang, Ngoc Phu Tran, Amin Milani Fard

This paper analyzes the security vulnerabilities of the Model Context Protocol (MCP), identifying tool poisoning as the most critical client-side threat, and proposes a multi-layered defense strategy.

View →
cs.CRcs.SERecentMar 23, 2026

Are AI-assisted Development Tools Immune to Prompt Injection?

Charoes Huang, Xin Huang, Amin Milani Fard

The paper empirically analyzes the susceptibility of seven widely used AI-assisted development tools (MCP clients) to prompt injection via tool-poisoning, revealing significant disparities in their se…

View →
cs.CRcs.SERecentMar 23, 2026

Auditing MCP Servers for Over-Privileged Tool Capabilities

Charoes Huang, Xin Huang, Amin Milani Fard

The paper introduces mcp-sec-audit, a comprehensive toolkit that assesses Model Context Protocol (MCP) servers for over-privileged and insecure tool capabilities.

View →