Logic Lock Protocol v1.2: Industrial-Grade Security Standard Against Gradient Attacks

The Logic Lock Protocol (LLP) is an industrial-grade security standard designed to protect machine learning and AI systems against gradient-based adversarial attacks. This protocol implements cryptographic locking mechanisms at the model parameter level, preventing unauthorized extraction or inversion through gradient leakage during training or inference phases. KEY FEATURES:- Cryptographic parameter locking using non-invertible transformations- Gradient noise injection with controlled entropy thresholds- Dynamic key rotation for long-term model security- Hardware-aware optimization for industrial deployment- API standardization for cross-framework compatibility APPLICATIONS:- Secure AI deployment in critical infrastructure- Protection of proprietary ML models in financial systems- Defense against model inversion attacks in healthcare AI- Intellectual property protection for research institutions TECHNICAL SPECIFICATIONS:- Protocol Version: 1.2- Security Level: Industrial Grade- Framework Support: PyTorch, TensorFlow, JAX- License: Apache 2.0

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-01
DOI
https://doi.org/10.5281/zenodo.18447076
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Logic Lock Protocol v1.2: Industrial-Grade Security Standard Against Gradient Attacks

FRANCO CARRICONDO
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Logic Lock Protocol v1.2: Industrial-Grade Security Standard Against Gradient Attacks

FRANCO CARRICONDO
article en

Abstract

The Logic Lock Protocol (LLP) is an industrial-grade security standard designed to protect machine learning and AI systems against gradient-based adversarial attacks. This protocol implements cryptographic locking mechanisms at the model parameter level, preventing unauthorized extraction or inversion through gradient leakage during training or inference phases. KEY FEATURES:- Cryptographic parameter locking using non-invertible transformations- Gradient noise injection with controlled entropy thresholds- Dynamic key rotation for long-term model security- Hardware-aware optimization for industrial deployment- API standardization for cross-framework compatibility APPLICATIONS:- Secure AI deployment in critical infrastructure- Protection of proprietary ML models in financial systems- Defense against model inversion attacks in healthcare AI- Intellectual property protection for research institutions TECHNICAL SPECIFICATIONS:- Protocol Version: 1.2- Security Level: Industrial Grade- Framework Support: PyTorch, TensorFlow, JAX- License: Apache 2.0

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 72%
Adversarial Robustness in Machine Learning
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