Natural Language Interface for Firewall Configuration
- URL: http://arxiv.org/abs/2512.10789v1
- Date: Thu, 11 Dec 2025 16:33:33 GMT
- Title: Natural Language Interface for Firewall Configuration
- Authors: F. Taghiyev, A. Aslanbayli,
- Abstract summary: This paper presents the design and prototype implementation of a natural language interface for configuring enterprise firewalls.<n>The framework allows administrators to express access control policies in plain language, which are then translated into vendor specific policies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents the design and prototype implementation of a natural language interface for configuring enterprise firewalls. The framework allows administrators to express access control policies in plain language, which are then translated into vendor specific configurations. A compact schema bound intermediate representation separates human intent from device syntax and in the current prototype compiles to Palo Alto PAN OS command line configuration while remaining extensible to other platforms. Large language models are used only as assistive parsers that generate typed intermediate representation objects, while compilation and enforcement remain deterministic. The prototype integrates three validation layers, namely a static linter that checks structural and vendor specific constraints, a safety gate that blocks overly permissive rules such as any to any allows, and a Batfish based simulator that validates configuration syntax and referential integrity against a synthetic device model. The paper describes the architecture, implementation, and test methodology on synthetic network context datasets and discusses how this approach can evolve into a scalable auditable and human centered workflow for firewall policy management.
Related papers
- REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry [0.0]
Large Language Models (LLMs) enable new forms of agentic automation.<n>We present REGAL, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry.
arXiv Detail & Related papers (2026-03-03T14:13:39Z) - AJAR: Adaptive Jailbreak Architecture for Red-teaming [1.356919241968803]
AJAR is a proof-of-concept framework designed to bridge the gap between "red-teaming" and "action security"<n>AJAR decouples adversarial logic from the execution loop, encapsulating state-of-the-art algorithms like X-Teaming as standardized, plug-and-play services.<n> AJAR is open-sourced to facilitate the standardized, environment-aware evaluation of this emerging attack surface.
arXiv Detail & Related papers (2026-01-16T03:30:40Z) - An Architecture-Led Hybrid Report on Body Language Detection Project [0.0]
This report provides an architecture-led analysis of two modern vision-language models (VLMs)<n>It explains how their architectural properties map to a practical video-to-artifact pipeline implemented in the BodyLanguageDetection.
arXiv Detail & Related papers (2025-12-28T18:03:00Z) - Monadic Context Engineering [59.95390010097654]
This paper introduces Monadic Context Engineering (MCE) to provide a formal foundation for agent design.<n>We demonstrate how Monads enable robust composition, how Applicatives provide a principled structure for parallel execution, and crucially, how Monad Transformers allow for the systematic composition of these capabilities.<n>This layered approach enables developers to construct complex, resilient, and efficient AI agents from simple, independently verifiable components.
arXiv Detail & Related papers (2025-12-27T01:52:06Z) - Zero-shot 3D Map Generation with LLM Agents: A Dual-Agent Architecture for Procedural Content Generation [8.398818816613806]
We propose a training-free architecture that utilizes LLM agents for zero-shot PCG parameter configuration.<n>Our system pairs an Actor agent with a Critic agent, enabling an iterative workflow where the system autonomously reasons over tool parameters.
arXiv Detail & Related papers (2025-12-11T10:22:02Z) - Prompt-to-Parts: Generative AI for Physical Assembly and Scalable Instructions [3.0620527758972496]
We present a framework for generating physically realizable assembly instructions from natural language descriptions.<n>Using LDraw as a text-rich intermediate representation, we demonstrate that large language models can be guided with tools to produce valid step-by-step construction sequences.<n>We introduce a Python library for programmatic model generation and evaluate buildable outputs on complex satellites, aircraft, and architectural domains.
arXiv Detail & Related papers (2025-12-10T05:55:33Z) - Prism: A Minimal Compositional Metalanguage for Specifying Agent Behavior [0.0]
Prism is a compositional metagrammar for specifying the behaviour of tool-using software agents.<n>Rather than introducing ad hoc control constructs, Prism is built around a fixed core context, Core1.<n>From a linguistic perspective, Prism enforces a clear separation between a reusable grammar-like core and domain specific lexicons.
arXiv Detail & Related papers (2025-11-29T19:52:21Z) - Prompt Decorators: A Declarative and Composable Syntax for Reasoning, Formatting, and Control in LLMs [0.0]
This paper introduces Prompt Decorators, a declarative, composable syntax that governs behavior through compact control tokens.<n>Each decorator modifies a behavioral dimension, such as verbose reasoning style, structure, or tone, without changing task content.<n>It defines a unified syntax, scoping model, and deterministic processing pipeline enabling predictable and auditable behavior composition.
arXiv Detail & Related papers (2025-10-21T17:35:49Z) - Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture [51.56484100374058]
We demonstrate that integrating semantic event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management systems.<n>The boldsea-engine's architecture interprets semantic models as executable algorithms without compilation.<n>It enables the modification of event models at runtime ensures transparency, and seamlessly merges data and business logic within a unified semantic framework.
arXiv Detail & Related papers (2025-09-11T18:12:46Z) - Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
arXiv Detail & Related papers (2025-08-28T13:00:28Z) - Policy as Code, Policy as Type [0.0]
We show how complex ABAC policies can be expressed as types in languages such as Agda and Lean.<n>We then go head-to-head with Rego, the popular and powerful open-source ABAC policy language.
arXiv Detail & Related papers (2025-06-02T09:04:48Z) - Targeted control of fast prototyping through domain-specific interface [28.96685079422302]
Industrial designers have long sought a natural and intuitive way to achieve the targeted control of prototype models.<n>Large Language Models have shown promise in this area, but their potential for controlling prototype models through language remains partially underutilized.<n>We propose an interface architecture that serves as a medium between the two languages.
arXiv Detail & Related papers (2025-06-02T01:56:31Z) - Type-Constrained Code Generation with Language Models [51.03439021895432]
We introduce a type-constrained decoding approach that leverages type systems to guide code generation.<n>For this purpose, we develop novel prefix automata and a search over inhabitable types, forming a sound approach to enforce well-typedness on LLM-generated code.<n>Our approach reduces compilation errors by more than half and significantly increases functional correctness in code synthesis, translation, and repair tasks.
arXiv Detail & Related papers (2025-04-12T15:03:00Z) - OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models [58.45517851437422]
Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding.<n>Existing solutions often rely on task-specific architectures and objectives for individual tasks.<n>In this paper, we introduce Omni V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis.
arXiv Detail & Related papers (2025-02-22T09:32:01Z) - From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control [58.72492647570062]
We introduce our method -- Learnable Latent Codes as Bridges (LCB) -- as an alternate architecture to overcome limitations.<n>We find that methodoutperforms baselines that leverage pure language as the interface layer on tasks that require reasoning and multi-step behaviors.
arXiv Detail & Related papers (2024-05-08T04:14:06Z) - HasTEE+ : Confidential Cloud Computing and Analytics with Haskell [50.994023665559496]
Confidential computing enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs)
TEEs offer low-level C/C++-based toolchains that are susceptible to inherent memory safety vulnerabilities and lack language constructs to monitor explicit and implicit information-flow leaks.
We address the above with HasTEE+, a domain-specific language (cla) embedded in Haskell that enables programming TEEs in a high-level language with strong type-safety.
arXiv Detail & Related papers (2024-01-17T00:56:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.