Secure Tool Manifest and Digital Signing Solution for Verifiable MCP and LLM Pipelines
- URL: http://arxiv.org/abs/2601.23132v1
- Date: Fri, 30 Jan 2026 16:22:21 GMT
- Title: Secure Tool Manifest and Digital Signing Solution for Verifiable MCP and LLM Pipelines
- Authors: Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel, Foutse Khomh, Amin Nikanjam, Mohammad Adnan Hamdaqa,
- Abstract summary: Large Language Models (LLMs) are increasingly adopted in sensitive domains such as healthcare and financial institutions' data analytics.<n>Existing control mechanisms, such as the Model Context Protocol (MCP), define compliance policies for tool invocation but lack verifiable enforcement and transparent validation of model actions.<n>We propose a novel Secure Tool Manifest and Digital Signing Framework, a structured and security-aware extension of Model Context Protocols.
- Score: 5.979408039210097
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) are increasingly adopted in sensitive domains such as healthcare and financial institutions' data analytics; however, their execution pipelines remain vulnerable to manipulation and unverifiable behavior. Existing control mechanisms, such as the Model Context Protocol (MCP), define compliance policies for tool invocation but lack verifiable enforcement and transparent validation of model actions. To address this gap, we propose a novel Secure Tool Manifest and Digital Signing Framework, a structured and security-aware extension of Model Context Protocols. The framework enforces cryptographically signed manifests, integrates transparent verification logs, and isolates model-internal execution metadata from user-visible components to ensure verifiable execution integrity. Furthermore, the evaluation demonstrates that the framework scales nearly linearly (R-squared = 0.998), achieves near-perfect acceptance of valid executions while consistently rejecting invalid ones, and maintains balanced model utilization across execution pipelines.
Related papers
- IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation [49.796717294455796]
We present IMMACULATE, a practical auditing framework that detects economically motivated deviations.<n>IMMACULATE selectively audits a small fraction of requests using verifiable computation, achieving strong detection guarantees while amortizing cryptographic overhead.
arXiv Detail & Related papers (2026-02-26T07:21:02Z) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
arXiv Detail & Related papers (2026-01-12T21:31:38Z) - ToolGate: Contract-Grounded and Verified Tool Execution for LLMs [35.000785781403515]
Large Language Models (LLMs) augmented with external tools have demonstrated remarkable capabilities in complex reasoning tasks.<n>Existing frameworks rely heavily on natural language reasoning to determine when tools can be invoked and whether their results should be committed.<n>We present textbfToolGate, a forward execution framework that provides logical safety guarantees and verifiable state evolution.
arXiv Detail & Related papers (2026-01-08T07:56:45Z) - Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation [0.0]
The current paper includes an example of agentic artificial intelligence (AI) based on autonomous software supply chain security.<n>It combines large language model (LLM)-based reasoning, reinforcement learning (RL), and multi-agent coordination.<n>Results show that agentic AI can facilitate the transition to self defending, proactive software supply chains.
arXiv Detail & Related papers (2025-12-29T14:06:09Z) - "Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems [2.2981698355892686]
This paper introduces ZKMLOps, a novel MLOps verification framework.<n>It operationalizes Zero-Knowledge Proofs (ZKPs)-cryptographic protocols allowing a prover to convince a verifier that a statement is true.<n>We evaluate the framework's practicality through a study of regulatory compliance in financial risk auditing.
arXiv Detail & Related papers (2025-10-30T15:03:32Z) - Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy [0.0]
We present Verifiable Fine Tuning, a protocol and system that produces succinct zero knowledge proofs.<n>We show that the system composes with probabilistic audits and bandwidth constraints.<n>Results indicate that the system is feasible today for real parameter efficient pipelines.
arXiv Detail & Related papers (2025-10-19T13:33:27Z) - DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
arXiv Detail & Related papers (2025-06-13T05:01:09Z) - Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
arXiv Detail & Related papers (2025-05-28T17:57:47Z) - Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [71.7892165868749]
Commercial Large Language Model (LLM) APIs create a fundamental trust problem.<n>Users pay for specific models but have no guarantee that providers deliver them faithfully.<n>We formalize this model substitution problem and evaluate detection methods under realistic adversarial conditions.<n>We propose and evaluate the use of Trusted Execution Environments (TEEs) as one practical and robust solution.
arXiv Detail & Related papers (2025-04-07T03:57:41Z) - Formal Verification of Permission Voucher [1.4732811715354452]
The Permission Voucher Protocol is a system designed for secure and authenticated access control in distributed environments.<n>The analysis employs the Tamarin Prover, a state-of-the-art tool for symbolic verification, to evaluate key security properties.<n>Results confirm the protocol's robustness against common attacks such as message tampering, impersonation, and replay.
arXiv Detail & Related papers (2024-12-18T14:11:50Z)
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.