# A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

arXiv: https://arxiv.org/abs/2501.09431 · HTML: https://mlsanity.com/paper/2501.09431/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- First posted on arXiv 16 Jan 2025.

## Placed in

- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Built on by (most cited first, 20 of 20)

- [CultureGuard: Towards Culturally-Aware Dataset and Guard Model for Multilingual Safety Applications](https://mlsanity.com/paper/2508.01710/) (2025)
- [Understanding How Value Neurons Shape the Generation of Specified Values in LLMs](https://mlsanity.com/paper/2505.17712/) (2025)
- [Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics](https://mlsanity.com/paper/2505.18658/) (2025)
- [What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices](https://mlsanity.com/paper/2504.03803/) (2025)
- [IMPersona: Evaluating Individual Level LM Impersonation](https://mlsanity.com/paper/2504.04332/) (2025)
- [A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy](https://mlsanity.com/paper/2506.09420/) (2025)
- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study](https://mlsanity.com/paper/2502.06567/) (2025)
- [Localizing Persona Representations in LLMs](https://mlsanity.com/paper/2505.24539/) (2025)
- [Controllable Value Alignment in Large Language Models through Neuron-Level Editing](https://mlsanity.com/paper/2602.07356/) (2026)
- [Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies](https://mlsanity.com/paper/2503.07306/) (2025)
- [Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation](https://mlsanity.com/paper/2503.16505/) (2025)
- [When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies](https://mlsanity.com/paper/2603.04259/) (2026)
- [From Craft to Constitution: A Governance-First Paradigm for Principled Agent Engineering](https://mlsanity.com/paper/2510.13857/) (2025)
- [Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai](https://mlsanity.com/paper/2601.01090/) (2026)
- [ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection](https://mlsanity.com/paper/2510.03502/) (2025)
- [Hiding in Plain Text: Detecting Concealed Jailbreaks via Activation Disentanglement](https://mlsanity.com/paper/2602.19396/) (2026)
- [Evolving Jailbreaks: Automated Multi-Objective Long-Tail Attacks on Large Language Models](https://mlsanity.com/paper/2603.20122/) (2026)
- [Are LLMs ready for HardChoices?](https://mlsanity.com/paper/2607.11471/) (2026)
- [From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers](https://mlsanity.com/paper/2609.21349/) (2026)

## Abstract (arXiv)

While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy leakage, hallucinated outputs, and value misalignment, and can be maliciously used for generating toxic content and unethical purposes after been jailbroken. Therefore, in this survey, we present a comprehensive review of recent advancements aimed at mitigating these issues, organized across the four phases of LLM development and usage: data collecting and pre-training, fine-tuning and alignment, prompting and reasoning, and post-processing and auditing. We elaborate on the recent advances for enhancing the performance of LLMs in terms of privacy protection, hallucination reduction, value alignment, toxicity elimination, and jailbreak defenses. In contrast to previous surveys that focus on a single dimension of responsible LLMs, this survey presents a unified framework that encompasses these diverse dimensions, providing a comprehensive view of enhancing LLMs to better serve real-world applications.