# dataRLsec: Safety, Security, and Reliability With Robust Offline Reinforcement Learning for DPAs

arXiv: https://arxiv.org/abs/2601.01289 · HTML: https://mlsanity.com/paper/2601.01289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 5 of 5)

- [Certified Defenses for Data Poisoning Attacks](https://mlsanity.com/paper/1706.03691/) (2017)
- [Data Poisoning Attacks on Federated Machine Learning](https://mlsanity.com/paper/2004.10020/) (2020)
- [De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks](https://mlsanity.com/paper/2105.03592/) (2021)
- [Data Poisoning Attacks to Local Differential Privacy Protocols](https://mlsanity.com/paper/1911.02046/) (2019)
- [detectGNN: Harnessing Graph Neural Networks for Enhanced Fraud Detection in Credit Card Transactions](https://mlsanity.com/paper/2503.22681/) (2025)

## Abstract (arXiv)

Data poisoning attacks (DPAs) are becoming popular as artificial intelligence (AI) algorithms, machine learning (ML) algorithms, and deep learning (DL) algorithms in this artificial intelligence (AI) era. Hackers and penetration testers are excessively injecting malicious contents in the training data (and in testing data too) that leads to false results that are very hard to inspect and predict. We have analyzed several recent technologies used (from deep reinforcement learning to federated learning) for the DPAs and their safety, security, & countermeasures. The problem setup along with the problem estimation is shown in the MuJoCo environment with performance of HalfCheetah before the dataset is poisoned and after the dataset is poisoned. We have analyzed several risks associated with the DPAs and falsification in medical data from popular poisoning data attacks to some popular data defenses. We have proposed robust offline reinforcement learning (Offline RL) for the safety and reliability with weighted hash verification along with density-ratio weighted behavioral cloning (DWBC) algorithm. The four stages of the proposed algorithm (as the Stage 0, the Stage 1, the Stage 2, and the Stage 3) are described with respect to offline RL, safety, and security for DPAs. The conclusion and future scope are provided with the intent to combine DWBC with other data defense strategies to counter and protect future contamination cyberattacks.