# The Ethics of Interaction: Mitigating Security Threats in LLMs arXiv: https://arxiv.org/abs/2401.12273 ยท HTML: https://mlsanity.com/paper/2401.12273/ ## Key facts - Cited by 33 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. - Builds on 3 of its 6 references that are in the map. - First posted on arXiv 22 Jan 2024. ## Placed in - LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/) ## Builds on (most cited first, 3 of 3) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity](https://mlsanity.com/paper/2301.12867/) (2023) - [Beyond the Safeguards: Exploring the Security Risks of ChatGPT](https://mlsanity.com/paper/2305.08005/) (2023) ## Built on by (most cited first, 30 of 33) - [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024) - [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024) - [Introducing v0.5 of the AI Safety Benchmark from MLCommons](https://mlsanity.com/paper/2404.12241/) (2024) - [VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.10704/) (2024) - [Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents](https://mlsanity.com/paper/2502.18509/) (2025) - [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025) - [UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models](https://mlsanity.com/paper/2502.13141/) (2025) - [On Catastrophic Inheritance of Large Foundation Models](https://mlsanity.com/paper/2402.01909/) (2024) - [Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents](https://mlsanity.com/paper/2408.02205/) (2024) - [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024) - [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024) - [LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models](https://mlsanity.com/paper/2505.19240/) (2025) - [DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding](https://mlsanity.com/paper/2410.16472/) (2024) - [LARGO: Latent Adversarial Reflection through Gradient Optimization for Jailbreaking LLMs](https://mlsanity.com/paper/2505.10838/) (2025) - [Building a Domain-specific Guardrail Model in Production](https://mlsanity.com/paper/2408.01452/) (2024) - [CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher](https://mlsanity.com/paper/2408.11650/) (2024) - [Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference](https://mlsanity.com/paper/2407.00075/) (2024) - [aiXamine: Simplified LLM Safety and Security](https://mlsanity.com/paper/2504.14985/) (2025) - [Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents](https://mlsanity.com/paper/2506.01344/) (2025) - [Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals](https://mlsanity.com/paper/2404.13885/) (2024) - [From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models](https://mlsanity.com/paper/2406.11106/) (2024) - [ChartLens: Fine-grained Visual Attribution in Charts](https://mlsanity.com/paper/2505.19360/) (2025) - [Localizing Persona Representations in LLMs](https://mlsanity.com/paper/2505.24539/) (2025) - [Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization System](https://mlsanity.com/paper/2505.01315/) (2025) - [CFSafety: Comprehensive Fine-grained Safety Assessment for LLMs](https://mlsanity.com/paper/2410.21695/) (2024) - [Quantifying Risk Propensities of Large Language Models: Ethical Focus and Bias Detection through Role-Play](https://mlsanity.com/paper/2411.08884/) (2024) - [dgMARK: Decoding-Guided Watermarking for Diffusion Language Models](https://mlsanity.com/paper/2601.22985/) (2026) - [360Zhinao Technical Report](https://mlsanity.com/paper/2405.13386/) (2024) - [Automated Harmfulness Testing for Code Large Language Models](https://mlsanity.com/paper/2503.16740/) (2025) - [Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection using Debias Tuning in Large Language Models](https://mlsanity.com/paper/2505.02252/) (2025) ## Abstract (arXiv) This paper comprehensively explores the ethical challenges arising from security threats to Large Language Models (LLMs). These intricate digital repositories are increasingly integrated into our daily lives, making them prime targets for attacks that can compromise their training data and the confidentiality of their data sources. The paper delves into the nuanced ethical repercussions of such security threats on society and individual privacy. We scrutinize five major threats--prompt injection, jailbreaking, Personal Identifiable Information (PII) exposure, sexually explicit content, and hate-based content--going beyond mere identification to assess their critical ethical consequences and the urgency they create for robust defensive strategies. The escalating reliance on LLMs underscores the crucial need for ensuring these systems operate within the bounds of ethical norms, particularly as their misuse can lead to significant societal and individual harm. We propose conceptualizing and developing an evaluative tool tailored for LLMs, which would serve a dual purpose: guiding developers and designers in preemptive fortification of backend systems and scrutinizing the ethical dimensions of LLM chatbot responses during the testing phase. By comparing LLM responses with those expected from humans in a moral context, we aim to discern the degree to which AI behaviors align with the ethical values held by a broader society. Ultimately, this paper not only underscores the ethical troubles presented by LLMs; it also highlights a path toward cultivating trust in these systems.