# Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks

arXiv: https://arxiv.org/abs/2409.08087 · HTML: https://mlsanity.com/paper/2409.08087/

## Key facts

- Cited by 26 papers in the map (data through 13 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 134 of its 158 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 30 of 134)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models](https://mlsanity.com/paper/2310.14566/) (2023)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection](https://mlsanity.com/paper/2004.07667/) (2020)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://mlsanity.com/paper/2105.03023/) (2021)
- [Scalable Extraction of Training Data from (Production) Language Models](https://mlsanity.com/paper/2311.17035/) (2023)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://mlsanity.com/paper/2312.00849/) (2023)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)

## Built on by (most cited first, 26 of 26)

- [Jailbreaking and Mitigation of Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2410.15236/) (2024)
- [Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements](https://mlsanity.com/paper/2502.12904/) (2025)
- [ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails](https://mlsanity.com/paper/2502.13458/) (2025)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices](https://mlsanity.com/paper/2504.03803/) (2025)
- [Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects](https://mlsanity.com/paper/2503.04783/) (2025)
- [From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice](https://mlsanity.com/paper/2410.01812/) (2024)
- [A Comprehensive Guide to Explainable AI: From Classical Models to LLMs](https://mlsanity.com/paper/2412.00800/) (2024)
- [From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models](https://mlsanity.com/paper/2411.05036/) (2024)
- [The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods](https://mlsanity.com/paper/2410.18866/) (2024)
- [Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models](https://mlsanity.com/paper/2411.05056/) (2024)
- [From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing](https://mlsanity.com/paper/2411.05826/) (2024)
- [From Noise to Nuance: Advances in Deep Generative Image Models](https://mlsanity.com/paper/2412.09656/) (2024)
- [Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review](https://mlsanity.com/paper/2603.18740/) (2026)
- [Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models](https://mlsanity.com/paper/2412.15748/) (2024)
- [You've Changed: Detecting Modification of Black-Box Large Language Models](https://mlsanity.com/paper/2504.12335/) (2025)
- [Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment](https://mlsanity.com/paper/2508.08629/) (2025)
- [AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression](https://mlsanity.com/paper/2512.13956/) (2025)
- [Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models](https://mlsanity.com/paper/2512.20677/) (2025)
- [Proactive Hardening of LLM Defenses with HASTE](https://mlsanity.com/paper/2601.19051/) (2026)
- [Is GPT-OSS All You Need? Benchmarking Large Language Models for Financial Intelligence and the Surprising Efficiency Paradox](https://mlsanity.com/paper/2512.14717/) (2025)
- [Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2602.01528/) (2026)
- [BadTemplate: A Training-Free Backdoor Attack via Chat Template Against Large Language Models](https://mlsanity.com/paper/2602.05401/) (2026)
- [EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control](https://mlsanity.com/paper/2606.28938/) (2026)
- [Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework](https://mlsanity.com/paper/2607.01581/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns. This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks. Issues related to inaccurate or misleading outputs from LLMs is discussed, with emphasis on the implementation from fact-checking methodologies to enhance response reliability. Inherent biases within LLMs are critically examined through diverse evaluation techniques, including controlled input studies and red teaming exercises. A comprehensive analysis of bias mitigation strategies is presented, including approaches from pre-processing interventions to in-training adjustments and post-processing refinements. The article also probes the complexity of distinguishing LLM-generated content from human-produced text, introducing detection mechanisms like DetectGPT and watermarking techniques while noting the limitations of machine learning enabled classifiers under intricate circumstances. Moreover, LLM vulnerabilities, including jailbreak attacks and prompt injection exploits, are analyzed by looking into different case studies and large-scale competitions like HackAPrompt. This review is concluded by retrospecting defense mechanisms to safeguard LLMs, accentuating the need for more extensive research into the LLM security field.