arXiv subjects

Hiran V Nath

Publications and source records attributed to Hiran V Nath.

3 recordsLinked to original sources

arXiv2026

From Hypervisor to Container: Cloud Security Vulnerabilities, Defense Mechanisms, and Open Challenges

In cloud computing, different users share the same physical hardware, which creates serious security risks. To protect data, cloud systems rely on virtual machines and containers to keep users isolated. This paper reviews over 120 security publications from 2008 to 2025, focusing on how these isolation boundaries can be breached. We examine threats like virtual machine escape, virtual machine hopping, CPU cache side-channels, container breakouts, vulnerable container images, and distributed denial of service (DDoS) attacks. We evaluate these security threats and their defenses using three key research questions. To compare different defense systems, we introduce a quantitative scoring framework called ADPO, which rates defenses from 0 to 3 based on their Accuracy, Deployment ease, Performance impact, and Operational overhead. We also map the impact of these attacks onto a 1-to-5 severity scale for Confidentiality, Integrity, and Availability. Finally, we highlight the trade-offs between security and system performance, and we outline open challenges like building low-overhead intrusion detection and creating realistic test datasets.

cs.CR↗

arXiv2026

Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives

LLM-powered autonomous agents are transforming the penetration testing space with dynamic, multi-step offensive security workflows that require minimal supervision by humans. These agents leverage sophisticated reasoning abilities and external security tools to independently carry out reconnaissance, identify vulnerabilities, devise exploitation plans, and perform post-exploitation operations. But the ability to have persistent memory, to take actions in the real world, and to do long-horizon reasoning raises qualitatively different security concerns than traditional chat-based LLM systems. Existing guardrail mechanisms for conversational AI may not be sufficient to secure autonomous AI pentesting agents accordingly. To address these issues, we carry out a comprehensive security analysis on autonomous AI-penetration testing agents. We systematically analyse representative agent architectures, characterise their trust boundaries and attack surfaces and propose a threat taxonomy that is aligned with the lifecycle and covers LLM lifecycle attacks, agent-architecture attacks and cross-cutting behavioural attacks. We analyse the limitations of existing guardrail mechanisms, identify key research gaps, and discuss future research directions for developing specialised, context-aware, and architecture-aware guardrails to secure next-generation AI-driven offensive security systems.

cs.CR↗

arXiv2026

Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement

The actions of frontline law enforcement officers in the initial hour of a cyber incident play a vital role in determining the ultimate success of an investigation. The minor mistakes they commit might result in irreversible critical impacts. The integrity of the investigation can be compromised, and the prosecution of cyber criminals can be hindered due to minor mistakes that happen in the initial hour. These are mainly because of the volatile nature of digital artifacts that might lead to procedural errors and evidence attrition. This paper provides a systematic survey of decision-support architectures designed to assist first responders of a cybercrime, categorizing them into playbooks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) frameworks, and Agentic AI systems. The survey critically considers the constraints of limited technical proficiency and inconsistent forensic infrastructure in a practical scenario. Our analysis identifies RAG-based systems as a relatively viable intermediate solution due to their natural language adaptability. However, significant risk factors like prompt sensitivity and the potential for confident hallucinations in legal contexts pose a major challenge. Furthermore, we review current benchmarks in cybersecurity and demonstrate that they are not sufficient to capture the specific safety and legal requirements of law enforcement, focusing on the initial hour of the cybercrime. We conclude by arguing for the necessity of a new evaluation benchmark focused on naive query robustness and evidence preservation, so as to ensure that AI-driven guidance aligns with the mandatory demands of judicial proceedings.

cs.CR↗