---
title: "A Survey of Safety and Trustworthiness of Large Language"
description: "The survey examines how to make large language models safe and trustworthy as they move… 374 references, cited by 41 papers here."
authors: ["Xiaowei Huang", "Wenjie Ruan", "Wei Huang", "Gaojie Jin", "Yi Dong", "Changshun Wu", "Saddek Bensalem", "Ronghui Mu", "Yi Qi", "Xingyu Zhao", "Kaiwen Cai", "Yanghao Zhang", "Sihao Wu", "Peipei Xu", "Dengyu Wu", "Andre Freitas", "Mustafa A. Mustafa"]
year: 2023
venue: "arXiv:2305.11391"
source: "https://arxiv.org/abs/2305.11391"
doi: "10.48550/arxiv.2305.11391"
arxiv: "2305.11391"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/"
---
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# A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

**Authors:** [Xiaowei Huang](https://md.research.elamur.ai/free/en/authors/xiaowei-huang/), [Wenjie Ruan](https://md.research.elamur.ai/free/en/authors/wenjie-ruan/), [Wei Huang](https://md.research.elamur.ai/free/en/authors/wei-huang/), Gaojie Jin, [Yi Dong](https://md.research.elamur.ai/free/en/authors/yi-dong/), [Changshun Wu](https://md.research.elamur.ai/free/en/authors/changshun-wu/), Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, [Andre Freitas](https://md.research.elamur.ai/free/en/authors/andre-freitas/) and Mustafa A. Mustafa · **Year:** 2023 · **Venue:** [arXiv:2305.11391](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/) ([Artificial Intelligence Review 2023](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/2023/)) · **Publisher:** [Springer Science+Business Media](https://md.research.elamur.ai/free/en/publishers/springer-science-business-media/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2305.11391) · [PDF](https://arxiv.org/pdf/2305.11391) · [DOI 10.48550/arxiv.2305.11391](https://doi.org/10.48550/arxiv.2305.11391) · [arXiv 2305.11391](https://arxiv.org/abs/2305.11391) · [OpenAlex](https://openalex.org/W4399750638)

**Citations:** 158 citations (OpenAlex, 2026-10-14) · [41](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The survey examines how to make large language models safe and trustworthy as they move quickly into industrial and consumer use. The authors note that many LLM failures have been documented, but few systematic methods exist for assessing them. They first sort these failures into three groups: inherent issues, attacks, and unintended bugs. They then ask whether Verification and Validation (V&V) methods could be adapted to LLMs and applied across the whole model lifecycle. These methods were developed for conventional software and for neural networks such as CNNs. The review covers four complementary areas: falsification and evaluation, verification, runtime monitoring, and regulation with ethical use. The authors conclude that research has concentrated on exposing weaknesses rather than on rigorous assurance. Existing techniques also struggle with three features of LLMs: their enormous size, their non-deterministic outputs, and training data scraped from the internet. The paper is useful as a structured entry point for anyone planning to deploy LLMs in settings where failures carry real consequences.

## Key points
- The survey draws on more than 370 references and organises them around a lifecycle assurance pipeline. The stages run from data synthesis and training through offline V&V, deployment, runtime monitoring and reliability assessment, ending with model enhancement.
- The vulnerability taxonomy has three branches:
  - **Inherent issues:** factual and reasoning errors, and sustainability.
  - **Attacks:** privacy leakage, robustness gaps, backdoors, and poisoning or disinformation.
  - **Unintended bugs:** accidental exposure of user data, and bias.
- The verification approaches reviewed for NLP models are interval bound propagation, abstract interpretation and randomised smoothing. The authors argue that black-box methods should be favoured, because white-box analysis does not scale to models of this size.
- RLHF and guardrails are singled out as key safety mechanisms. The authors point out that guardrail designs are undisclosed and may themselves need verification.
- Five future directions are proposed: data privacy, modelling the real-world consequences of failures, rigorous engineering, provable (e.g., statistical) guarantees, and practical standards that complement regulation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Manchester](https://md.research.elamur.ai/free/en/universities/university-of-manchester/) — University, Manchester, United Kingdom · 93 works · 45,138 citations
- [KU Leuven](https://md.research.elamur.ai/free/en/universities/ku-leuven/) — University, Leuven, Belgium · 46 works · 7,833 citations
- [Université Grenoble Alpes](https://md.research.elamur.ai/free/en/universities/universite-grenoble-alpes/) — University, Saint-Martin-d'Hères, France · 24 works · 8,259 citations
- [University of Liverpool](https://md.research.elamur.ai/free/en/universities/university-of-liverpool/) — University, Liverpool, United Kingdom · 16 works · 745 citations
- [Purple Mountain Laboratories](https://md.research.elamur.ai/free/en/labs/purple-mountain-laboratories/) — Research institute, Nanjing, China · 3 works · 200 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [⚙️ Engineering](https://md.research.elamur.ai/free/en/shelves/engineering/) · 188 works · [💻 Software](https://md.research.elamur.ai/free/en/shelves/computer-science/software/) · 119 works · [⚙️ Safety, Risk, Reliability and Quality](https://md.research.elamur.ai/free/en/shelves/engineering/safety-risk-reliability-and-quality/) · 14 works

🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works · [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 587 works

## Cite

Cite as: Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas and Mustafa A. Mustafa. 2023. A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation. arXiv:2305.11391.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{huang2023survey,
  title = {{A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation}},
  author = {Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas and Mustafa A. Mustafa},
  year = {2023},
  doi = {10.48550/arxiv.2305.11391},
  eprint = {2305.11391},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.11391}
}
```

- [← previous Mathematical Discoveries from Program Search with Large Language Models](https://md.research.elamur.ai/free/en/papers/paredes-2023-mathematical-discoveries-program-search-large/)
- [next → MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/4/)

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*Updated 2 November 2026*