An open-source, privacy-focused, user-facing firewall designed to secure both web-based and programmatic LLM interactions and its layered architecture enables deployment across heterogeneous environments, allowing organizations to balance computational cost, detection depth and latency.
Abstract
While Large Language Models (LLMs) have become essential productivity tools, their integration into workflows without adequate safeguards creates significant risks. This paper proposes an open-source, privacy-focused, user-facing firewall designed to secure both web-based and programmatic LLM interactions. The architecture combines a browser extension and a proxy for total traffic interception across both HTTP(S) and WebSocket communications. At its core, a flexible multi-agent pipeline delivers data leakage prevention through a hybrid approach combining deterministic detectors with LLM-driven semantic analysis, proprietary code leakage prevention, and extensible components designed for future security enhancements such as prompt injection evasion. The framework's layered architecture enables deployment across heterogeneous environments, allowing organizations to balance computational cost, detection depth and latency. Evaluation results demonstrate it achieves F1 scores of up to 94.93% on optimal configurations.
A privacy-preserving hybrid middleware architecture that enforces a local trust boundary as its primary design constraint that is model-agnostic, requires no retraining of the underlying LLM, and is compatible with black-box API deployments is proposed and evaluated.
Adam Ait Hsine, A. Arabo· Electronics· 0 citations
Agentic large language model (LLM) systems gain much of their practical value from tool calling, the capacity to invoke external functions such as web searches, database lookups, and application programming interface (API) requests during multi-step reasoning. Deploying such agents inside Trusted Execution Environments (TEEs) creates a structural tension: the confidential virtual machine (CVM) that protects user data must remain isolated from the host infrastructure, yet the agent must reach beyond the enclave boundary to be useful. The Model Context Protocol (MCP), which is now the main open standard for connecting LLM applications to external tools and data sources, was not designed with TEE constraints in mind, leaving three critical incompatibilities unresolved: transport mechanisms that expose user-derived parameters to untrusted hosts, dynamic capability discovery that violates pre-deployment transparency requirements, and authentication models misaligned with non-targetability guarantees. This paper presents Confidential MCP (C-MCP), a set of backward-compatible extensions to MCP that enable standardized, auditable tool calling within and across TEE boundaries. C-MCP introduces a three-zone enclave-partitioned server topology, a programmable Anonymization Transform Layer (ATL) with formal parameter classification and entropy bounds, and Attested Egress Policies (AEPs) that extend behavioral transparency from static binary attestation to constraints on verifiable runtime tool invocation. We analyze open-source LLM deployment challenges, including tool-calling information minimality, TEE inference overhead accumulation across agentic reasoning steps, and model supply chain integrity, and present concrete domain case studies in healthcare, legal practice, and financial services.
Ankur Aggarwal· International journal of com...· 0 citations
MTGuard is proposed, a hybrid analysis-based defense framework designed to safeguard the use of MCP tools in LLM agents by leveraging lifecycle-aware static-dynamic co-analysis and effectively mitigates multiple categories of harmful tool use across different LLM agents while maintaining performance on benign user tasks.
Ping He, Yuexiang Xie, Yaliang Li et al.· arXiv.org· 0 citations
This study designs a comprehensive testbed and a layered defense, Spotlight-Guard, that combines spotlighting-based input isolation, an LLM detection-and-quarantine pipeline, and instruction integrity based on a Hash-based Message Authentication Code into a single framework, and it is evaluated jointly along two axes: security and LLM performance.
Doygun Demirol, Murat Aydoğan· Applied Sciences· 0 citations
AEGIS is presented, a policy enforcement component that enables administrators to define fine-grained safeguards against resource abuse across heterogeneous MCP tools and modalities and detects and mitigates abusive behaviors while preserving the flexibility of MCP-based agent ecosystems.
S. Priya, Teryl Taylor, F. Araujo· 2026 56th Annual IEEE Intern...· 0 citations
The results show that plan-first execution combined with label-preserving persistence can substantially strengthen persistent LLM agents, while revealing an important security-utility tradeoff introduced by strict integrity enforcement.
Dylan Girrens, Guang-Jing Wang· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
