mlsanityData to 14 Oct 2026

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

arXiv 2403.12503 ↗First posted 19 Mar 2024cs.CR cs.AI cs.LGcited by 37

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Abstract (arXiv)

Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations. Nevertheless, alongside their remarkable utility, LLMs introduce critical security and risk considerations. These challenges warrant careful examination to ensure responsible deployment and safeguard against potential vulnerabilities. This research paper thoroughly investigates security and privacy concerns related to LLMs from five thematic perspectives: security and privacy concerns, vulnerabilities against adversarial attacks, potential harms caused by misuses of LLMs, mitigation strategies to address these challenges while identifying limitations of current strategies. Lastly, the paper recommends promising avenues for future research to enhance the security and risk management of LLMs.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). Builds on 120 of its 155 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

120 of 155 references.

References · 120

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  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
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38 more
  1. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  2. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,786
  3. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  4. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
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  12. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  13. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 726
  14. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  15. 2023Baseline Defenses for Adversarial Attacks Against Aligned Language Modelscited by 651
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50 most cited of 120 All 120 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 11; 2025: 19; 2026: 7.

37 papers.

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  1. 2025CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilitiescited by 96
  2. 2024Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)cited by 46
  3. 2024The Future of Large Language Model Pre-training is Federatedcited by 30
  4. 2024Security of AI Agentscited by 26
  5. 2025Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Modelscited by 21
  6. 2025Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystemcited by 19
  7. 2024Causal Evaluation of Language Modelscited by 18
  8. 2026SoK: The Attack Surface of Agentic AI - Tools and Autonomycited by 16
  9. 2025Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignmentcited by 14
  10. 2024Cognitive Overload Attack:Prompt Injection for Long Contextcited by 13
  11. 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigationcited by 10
  12. 2024Blockchain for Large Language Model Security and Safety: A Holistic Surveycited by 8
25 more
  1. 2025LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasurescited by 8
  2. 2024VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMscited by 7
  3. 2024PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMscited by 7
  4. 2026Understanding Emergent Misalignment via Feature Superposition Geometrycited by 7
  5. 2025Real-World Gaps in AI Governance Researchcited by 5
  6. 2025Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguardscited by 5
  7. 2025A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysiscited by 5
  8. 2025Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encodingcited by 5
  9. 2025A First Look at the Security Issues in the Model Context Protocol Ecosystemcited by 5
  10. 2026ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefensecited by 4
  11. 2024Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspectivecited by 3
  12. 2026SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendationscited by 3
  13. 2024On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMscited by 2
  14. 2025SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deploymentcited by 2
  15. 2025Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization Systemcited by 2
  16. 2025Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessmentcited by 2
  17. 2026Layered LLM Defenses as an Ensemble: Access Tiers, Inference Cost, and the Measured Failure Correlation Between Defense Layerscited by 2
  18. 2025Self-reflecting Large Language Models: A Hegelian Dialectical Approachcited by 1
  19. 2025Differential Robustness in Transformer Language Models: Empirical Evaluation Under Adversarial Text Attackscited by 1
  20. 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1
  21. 2024"Moralized" Multi-Step Jailbreak Prompts: Black-Box Testing of Guardrails in Large Language Models for Verbal Attackscited by 0
  22. 2025AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)cited by 0
  23. 2025HATS: High-Accuracy Triple-Set Watermarking for Large Language Modelscited by 0
  24. 2026Diagnosing Generalization Failures in Fine-Tuned LLMs: A Cross-Architectural Study on Phishing Detectioncited by 0
  25. 2026Adversarial Prompting Framework for AI Safety Assessmentcited by 0