The widespread adoption of Large Language Models (LLMs) in software development has accelerated coding workflows, with tools like GitHub Copilot and Google Gemini reducing development time by up to 55.8%. However, these systems suffer from security and trustworthiness challenges. In this paper, we demonstrate a novel attack - ''Vulnerability propagation attack'' in the conext of the code generated by LLMs. We present a formal framework for evaluating vulnerability propagation in LLM-assisted development, focusing on two key metrics: Vulnerability Carry Rate (VCR), which quantifies the intra-session propagation of vulnerabilities, and Context Retention Factor (CRF), which measures cross-session persistence. Using three leading LLMs -- GPT-4, Sonar, and Gemini -- we evaluate 10 critical Common Weakness Enumeration (CWE) categories across three tiers of prompts (direct injection, implicit vulnerabilities, and adversarial few-shot prompting). Our findings reveal that adversarial few-shot prompting exacerbates vulnerability propagation, with VCR values reaching 100% under high-difficulty conditions and CRF values demonstrating significant retention across sessions. These results underscore the need for session-aware security measures and robust vulnerability detection frameworks in AI-assisted development environments.

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, semantic webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes - HMACs for graphs (gHMAC) for two-party data sharing, and redactable HMACs for graphs (rgHMAC) for third-party data sharing, such as a cloud-based graph database service. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experimental results corroborate that the proposed HMAC-based schemes for graphs are highly efficient as compared to the digital signature-based schemes - computation of HMAC tags is about 10 times faster than the computation of digital signatures.

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, Semantic Webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes-HMACs for graphs (gHMAC) for two-party data sharing, and redactable HMACs for graphs (rgHMAC) for third-party data sharing, such as a cloud-based graph database service. We compute one HMAC value for both the schemes and two other verification objects for rgHMAC scheme that are shared with the verifier. We show that the proposed schemes are provably secure with respect to integrity attacks on the structure and/or content of graphs and query results. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experimental results corroborate that the proposed HMAC-based schemes for graphs are highly efficient as compared to the digital signature-based schemes-computation of HMAC tags is about 10 times faster than the computation of digital signatures.

Deploying applications in containers has several advantages, such as rapid development, portability across different machines, and simplified maintenance. In a cloud computing environment, container scheduling algorithms coordinate with different aspects of physical systems, such as memory allocation for tasks of different users. The scheduled containers on a host may process sensitive data. For instance, containers may process healthcare information. In that case, diverse cloud environments with different components and subsystems may lead to a potential personal health information leakage and violation of data privacy. In this paper, we introduce a novel compliance-aware analysis model for provisioning containers in the cloud, that provides a HIPAA compliance model. The proposed method dynamically analyzes different requirements of HIPAA complaint containers (HIPAA parameters) and their associated risk values. Based on the risk optimization of the compliance parameters for data security and data privacy of the containers, our proposed method determines scheduling of containers that offer the lowest risk to healthcare data and to the compliance posture of the container. The model describes the resources that are associated with highlevel risks and provides real-time resource recommendation for a container scheduler to decrease the risk of HIPAA compliance violation.

Software Defined Networking (SDN) is increasingly being used in data centers as well as enterprise networks. In an environment that has strict compliance requirements, such as HIPAA compliance, a critical role for an SDN controller is to route all data packets while considering data privacy preservation and compliance-preservation. In this paper, we address this problem by proposing a routing protocol for SDN which is an efficient risk-based swarm routing protocol. The programmable capability of controllers is exploited in order to minimize privacy and compliance risks in data transmission. The proposed routing protocol is based on the Ant Colony Optimization technique and machine learning, while the data for learning is obtained from OVSDB and the OpenvSwitch Database management protocol. We collect a history of packet transfers for training purposes and learn from the training data to efficiently and intelligently route sensitive data packets while it preserves the target compliance. This routing is obtained by intelligent eviction of rules that are downloaded to the switches. We have implemented the proposed schemes based on an RYU controller.