The Content-Centric Network (CCN) presents an alternative to the conventional TCP/IP network, where IP is fundamental for communication between the source and destination. Instead of relying on IP addresses, CCN emphasizes content to enable efficient data distribution through caching and delivery. The increasing demand of graphic-intensive applications requires minimal response time and optimized resource utilization. Therefore, the CCN plays a vital role due to its efficient architecture and content management approach. To reduce data retrieval delays in CCNs, traditional methods improve caching mechanisms through clustering. However, these methods do not address the optimal use of resources, including CPU, memory, storage, and available links, along with the incorporation of social awareness. This study proposes SARAC4N, a socially and resource-aware caching framework for clustered Content-Centric Networks that integrates dual-head clustering and popularity-driven content placement. It enhances caching efficiency, reduces retrieval delays, and improves resource utilization across heterogeneous network topologies. This approach will help resolve congestion issues while enhancing social awareness, lowering error rates, and ensuring efficient content delivery. The proposed Socially and Resource-Aware Caching in Clustered Content-Centric Network (SARAC4N) enhances caching effectiveness by optimally utilizing resources and positioning them with social awareness within the cluster. Furthermore, it enhances metrics such as data retrieval time, reduces computation and memory usage, minimizes data redundancy, optimizes network usage, and lowers storage requirements, all while maintaining a very low error rate.

Large language model (LLM)-powered retrieval-augmented generation (RAG) systems significantly reduce factual errors in question answering, but they present a novel and under-investigated attack surface: factual information in retrieved text can be exposed in ways that do not align with the disclosure norms of the information itself. In legal, medical, and enterprise environments, this leakage is not only a confidentiality violation but a contextual integrity (CI) violation, where privacy is understood as the appropriate flow of information between roles and for specific purposes. Current defenses are mostly input minimizers or post hoc output filters that are blind to the circumstances of the data source (sender, recipient, purpose), creating a structural gap between the document store and the model generation step. We introduce ContextGuard-RAG, a multi-agent privacy enforcement framework that embeds CI theory directly into the retrieval and generation pipeline. It consists of three tightly coupled components: a CI-policy encoder that attaches sender, recipient, subject, information-type, and transmission-principle norms to retrieved contexts from document metadata; a privacy-aware reranker that filters contexts violating inferred CI norms using a fine-tuned cross-encoder classifier; and a generative firewall agent that sanitizes output using reinforcement learning to avoid transitive leakage through document-derived hallucinations or indirect inferences. On PrivacyQA, MedQA, and LegalBench, ContextGuard-RAG reduces CI violations by about 34 percent relative to vanilla RAG, by 13.5 percent on average over AirGapAgent, and by 10 percent on average over the 1-2-3 Check multi-agent reasoning approach, while achieving parity with the best accuracy baseline in ROUGE-L (within 0.6 absolute points) and remaining stable under context-hijacking adversarial probes. Unlike prior approaches, ContextGuard-RAG performs norm-aware retrieval rather than post hoc filtering and benefits four backbone LLMs without retraining the upstream agents.

Agentic large language model (LLM) networks are increasingly used in safety-critical settings where autonomous agents invoke tools, exchange context, and coordinate decisions. Prompt-injection attacks remain a significant threat to these multi-agent pipelines because they can compromise data flows between agents, bypass instruction hierarchies, and corrupt output integrity. Although defenses against injected prompts and mechanisms for cryptographically verifying model-related computations have been studied independently, no common framework unifies these complementary security perspectives in a protocol suitable for real-time agentic deployments. From the perspective of symmetry, secure inter-agent communication requires the preservation of an invariant integrity relationship between a message at its source and the corresponding message accepted at its destination. A benign communication path therefore exhibits a form of integrity symmetry, whereas prompt injection or message manipulation creates an asymmetric state in which the received payload, its semantic effect, or the receiving model’s internal activation pattern deviates from the trusted reference state. In this paper, we propose SecurePrompt-IntegrityNet (SPI-Net), a prompt-injection-resilient data integrity verification protocol that combines cryptographic attestation with anomaly-aware activation monitoring. SPI-Net provides three closely related mechanisms: a Merkle-tree-based commitment system that verifies the provenance and integrity of data payloads exchanged between agents; a layer-wise Mahalanobis-scoring Activation Anomaly Detector (AAD) that identifies distributional shifts in the intermediate representations of LLMs; and a Trust Propagation Consensus (TPC) mechanism that combines cryptographic and behavioral evidence into per-payload integrity verdicts. In this formulation, the Cryptographic Attestation Module (CAM) tests whether message-level structural symmetry is preserved between the sender and receiver, whereas the AAD detects behavioral symmetry breaking in activation space. Experiments on three multi-agent benchmarks under five adaptive attack strategies show that SPI-Net achieves a 96.8% detection rate with a 1.7% false positive rate, reduces the attack success rate by 94.3% relative to undefended baselines, verifies data integrity with 99.2% accuracy, and introduces only 38 ms of median per-message latency. These results demonstrate that jointly preserving cryptographic integrity symmetry and identifying activation-level asymmetry provides substantially stronger prompt-injection resilience than either verification mechanism alone.