Computer Science ›› 2026, Vol. 53 ›› Issue (7): 381-396.doi: 10.11896/jsjkx.250600196

• Information Security • Previous Articles     Next Articles

Survey on Security Risks and Mitigation Strategies for Generative Artificial Intelligence

CHEN Quantao, ZHANG Yangsen, WANG Pu, GUO Yalong   

  1. Institute of Intelligent Information Processing,Beijing Information Science and Technology University,Beijing 100192,China
  • Received:2025-06-26 Revised:2025-10-14 Online:2026-07-15 Published:2026-07-10
  • About author:CHEN Quantao,born in 2001,postgra-duate.His main research interests include natural language processing and network information security.
    ZHANG Yangsen,born in 1962,postdoctoral fellow,professor,Ph.D supervisor,is a distinguished member of CCF(No.16640D).His main research in-terest is natural language processing.
  • Supported by:
    National Natural Science Foundation of China(62176023).

Abstract: With the widespread application of GAI(Generative Artificial Intelligence) technologies across multimodal domains such as image,text,and audio generation,the associated security risks and governance challenges have become increasingly prominent.This paper aims to systematically review the core security issues faced by GAI and to provide an in-depth analysis from two dimensions:the data layer and the model layer.At the data layer,five major categories of risks are examined,including privacy leakage,data tampering,unreliable data sources,bias and unfairness,and data sovereignty and compliance.At the model layer,seven major security threats are analyzed,namely adversarial examples,deepfakes,prompt attacks,backdoor attacks,model stea-ling attacks,model inversion attacks,and model jailbreak attacks.In response to these issues,this paper further summarizes recent advances in defense technologies from both academia and industry,covering differential privacy(DP),homomorphic encryption(HE),anomaly detection,access control,adversarial robustness,model watermarking,machine unlearning(MU),and knowledge editing.Finally,based on the above discussion,the paper outlines future challenges and research directions in the field of GAI security,with the aim of providing useful insights and a reference framework for the continued exploration of GAI security.

Key words: Generative artificial intelligence, Security risks, Data privacy, Adversarial examples, Defense strategies

CLC Number: 

  • TP309
[1]SINGH S,SINGH S,KRAUS S,et al.Characterizing generative artificial intelligence applications:Text-mining-enabled technology roadmapping[J].Journal of Innovation and Knowledge,2024,9(3):100531.
[2]LI Z,HUANG C,QIU W.An intrusion detection method combining variational auto-encoder and generative adversarial networks[J].Computer Networks,2024,253:110724.
[3]CHEN J Y,XI C K,ZHENG H B,et al.A Survey on the Security of Multimodal Large Language Models [J].Journal of Computer Science,2025,52(7):315-341.
[4]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Advances in Neural Information Processing Systems.2014.
[5]MA Z,ZHANG Y,JIA G,et al.Efficient Diffusion Models:A Comprehensive Survey from Principles to Practices [J].arXiv:2410.11795,2024.
[6]HO J,JAIN A,ABBEEL P.Denoising Diffusion ProbabilisticModels [J].arXiv:2006.11239,2020.
[7]LUKOIANOV A,DE OCÁRIZ BORDE H S,GREENEWALD K,et al.Score Distillation via Reparametrized DDIM[C]//Proceedings of Advances in Neural Information Processing Systems.New York:Curran Associates Inc.,2024:26011-26044.
[8]CHEN M S,MEI S,FAN J Q,et al.Opportunities and challenges of diffusion models for generative AI[J].National Science Review,2024,11(12):nwae348.
[9]JIANG Y,YANG Y,YIN J L,et al.A survey on security and privacy risks in large language models[J].Journal of Computer Research and Development,2025,62(8):1979-2018.
[10]ZHANG X L,QU X Y,XIE J F,et al.Overview of Artificial Intelligence Generated ContentTechnologies [J/OL].Big Data Research,2025:1-37.[2025-08-14].https://link.cnki.net/urlid/10.1321.G2.20250408.1439.004.
[11]HAO Y H.Analysis and Governance of Artificial Intelligence Security Risks [J].Journal of China Academy of Electronics and Information Technology,2020,15(6):501-505.
[12]ZHU B,MU N,JIAO J,et al.Generative AI Security:Challenges and Countermeasures [J].arXiv:2402.12617,2024.
[13]FERRAG M A,ALWAHEDI F,BATTAH A,et al.Generative AI in Cybersecurity:A Comprehensive Review of LLM Applications and Vulnerabilities [J].Internet of Things and Cyber-Physical Systems,2025,5:1-46.
[14]SANDMANN S,HEGSELMANN S,FUJARSKI M,et al.Benchmark evaluation of DeepSeek large language models in clinical decision-making[J].Nature Medicine,2025,31(8):2546-2549.
[15]HU T,KYRYCHENKO Y,RATHJE S,et al.Generative language models exhibit social identity biases[J].Nature Computational Science,2025,5:65-75.
[16]CANCELA-OUTEDA C.The EU's AI act:A framework for collaborative governance[J].Internet of Things,2024,27:101291.
[17]HOWELLM D.Generative artificial intelligence,patient safetyand healthcare quality:A review[J].BMJ Quality & Safety,2024,33(11):748-754.
[18]UDDIN M,IRSHAD M S,KANDHRO I A,et al.Generative AI revolution in cybersecurity:a comprehensive review of threat intelligence and operations[J].Artificial Intelligence Review,2025,58(8):236.
[19]KHAN N,NGUYEN T,BERMAK A,et al.Unmasking Syn-thetic Realities in Generative AI:A Comprehensive Review of Adversarially Robust Deepfake Detection Systems [J].arXiv:2507.21157,2025.
[20]YAN Z,ZHANG Y,FAN Y,et al.UCF:Uncovering Common Features for Generalizable Deepfake Detection[C]//2023 IEEE/CVF International Conference on Computer Vision(ICCV).Piscataway,NJ:IEEE,2023:22355-22366.
[21]PEREZ E,HUANG S,SONG F,et al.Red Teaming Language Models with Language Models [J].arXiv:2202.03286,2022.
[22]LIU Y,JIA Y,GENGR,et al.Formalizing and Benchmarking Prompt Injection Attacks and Defenses[C]//Proceedings of the 33rd USENIX Security Symposium.Philadelphia,PA:USENIX Association,2024:1831-1847.
[23]KANG D,LI X,STOICA I,et al.Exploiting programmatic behavior of LLMs:Chain-of-thought injections[C]//2024 IEEE Symposium on Security and Privacy Workshops.IEEE,2024:132-143.
[24]LEI T,ZHANG Y,WANG X,et al.Jailbreaking black box large language models in twenty queries[C]//Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security.New York:ACM,2024:2423-2437.
[25]HUANG H,ZHAO Z Y,BACKES M,et al.Composite Back-door Attacks Against Large Language Models[C]//Proceedings of the Findings of the Association for Computational Linguistics:NAACL 2024.Mexico City:Association for Computational Linguistics,2024:1459-1472.
[26]ZHAO S,JIA M H Z,GUO Z L,et al.A Survey of Recent Backdoor Attacks and Defenses in Large Language Models[J].ar-Xiv:2406.06852,2024.
[27]QIN Z,ZHUANG T M,ZHU G S,et al.A Survey of Security Attacks and Defense Strategies for Artificial Intelligence Models [J].Journal of Computer Research and Development,2024,61(10):2627-2648.
[28]JIAO W,HUANG J,WANG W,et al.ParroT:Translating du-ring chat using large language models tuned with human translation and feedback[C]//Proceedings of the Findings of the Association for Computational Linguistics:EMNLP 2023.Association for Computational Linguistics,2023:15009-15020.
[29]LIU Y,WU C,YUAN K,et al.Targeted model extraction from API-based ML services[C]//Proceedings of the 2024 IEEE Symposium on Security and Privacy.IEEE,2024:1843-1858.
[30]PAN X D,ZHANG M,JI S L,et al.Privacy risks of general-purpose language models[C]//Proceedings of the 41st IEEE Symposium on Security and Privacy(SP).Piscataway,NJ:IEEE,2020:1314-1331.
[31]SONG C Z,RAGHUNATHAN A.Information leakage in embedding models[C]//Proceedings of the 27th ACM SIGSAC Conference on Computer and Communications Security.New York:ACM,2020:377-390.
[32]LI H,XU M S,SONG Y Q.Sentence Embedding Leaks More Information than You Expect:Generative Embedding Inversion Attack to Recover the Whole Sentence [J].arXiv:2305.03010,2023.
[33]ZHANG R,HIDANO S,KOUSHANFAR F.Text Revealer:Private Text Reconstruction via Model Inversion Attacks against Transformers [J].arXiv:2209.10505,2022.
[34]SHEN X Y,CHEN Z Y,BACKES M,et al.“Do Anything Now”:Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models [J].arXiv:2308.03825,2023.
[35]LI H,GUO D D,FAN W,et al.Multi-step Jailbreaking Privacy Attacks on ChatGPT [J].arXiv:2304.05197,2023.
[36]YANG A,YANG TA.Social Dangers of Generative Artificial Intelligence:Review and Guidelines[C]//Proceedings of the 25th Annual International Conference on Digital Government Research.2024:654-658.
[37]CHUA J,LI Y,YANG S Y,et al.AI Safety in Generative AI Large Language Models:A Survey [J].arXiv:2407.18369,2024.
[38]BU Z,ZHANG X,ZHA S,et al.Pre-training Differentially Private Models with Limited Public Data[C]//Proceedings of the 37th Conference on Neural Information Processing Systems(NeurIPS 2024).Curran Associates Inc.,2024:94652-94683.
[39]NAHID M M H,HASAN S B.SafeSynthDP:Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy [J].arXiv:2412.20641,2024.
[40]WANG H,XU Q,ZHANG Q H,et al.LFDP:Low-Frequency Information Fusion for Differential Privacy Robustness Enhancement [J].Journal of Information Security,2025,10(1):47-60.
[41]WU X W,GONG L,XIONG D Y.Adaptive differential privacy for language model training[C]//Proceedings of the First Workshop on Federated Learning for Natural Language Processing.Association for Computational Linguistics,2022:21-26.
[42]LU X Z,LIU Z H,XIAO L,et al.Reinforcement Learning-Based Personalized Differentially Private Federated Learning[J].IEEE Transactions on Information Forensics and Security,2025,20:465-477.
[43]AZIZ R,BANERJEE S,BOUZEFRANE S.Privacy preservingfederated learning:a novel approach for combining differential privacy and homomorphicencryption[C]//Information Security Theory and Practice:14th IFIP WG 11.2 International Confe-rence,WISTP 2024.Springer,2024:162-177.
[44]BRAKERSKI Z,VAIKUNTANATHAN V.Efficient Fully Homomorphic Encryption from(Standard) LWE [EB/OL].(2011-06-26) [2025-05-21].https://eprint.iacr.org/2011/344.
[45]GUO K Y,HAN Y L,WU R M.Revocable Hierarchical Attri-bute-Based Encryption Scheme Based on RLWE [J].Information Technology and Network Security,2021,40(8):9-16.
[46]DOBRAUNIG C,GRASSI L,HELMINGER L,et al.Pasta:a case forhybrid homomorphic encryption[C]//Proceedings of the 33rd USENIX Security Symposium.Berkeley:USENIX Association,2024:1-18.
[47]MATSUMOTO M,NOZAKI A,TAKASE H,et al.TFHE-SBC:Software Designs for Fully Homomorphic Encryption over the Torus on Single Board Computers [J].arXiv:2503.02559,2025.
[48]CHEN D,QU H,XU G.AegisFL:Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning[C]//Proceedings of the 41st International Conference on Machine Learning.PMLR,2024:7207-7219.
[49]RIAZ R,HAN G,SHAUKAT K,et al.A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments [J].Scientific Reports,2025,15(1):26786.
[50]CHEN L,JIANG H,WANG L,et al.Generative adversarialsynthetic neighbors-based unsupervised anomaly detection [J].Scientific Reports,2025,15:16.
[51]LIGUORI A,RITACCO E,PISANI F S,et al.Robust anomaly detection via adversarial counterfactual generation[J].Know-ledge and Information Systems,2024,66:7437-7468.
[52]FANG Q Q,SU Q L,LYU W X,et al.Boosting Fine-Grained Vi-sual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2025:16532-16540.
[53]SUN H L,HUANG Y,HAN L S,et al.MTS-DVGAN:Anomaly detection in cyber-physical systems using a dual variational generative adversarial network[J].Computers & Security,2024,139:103570.
[54]ZHAO Y.Anomaly Hybrid:A Domain-agnostic Generative Fra-mework for General Anomaly Detection[J].arXiv:2504.04340,2025.
[55]CAO Y,XU X,SUN C,et al.Towards generic anomaly detection and understanding:large-scale visual-linguistic model(GPT-4V) takes the lead [J].arXiv:2311.02782,2023.
[56]FERETZAKIS G,VERYKIOS V S.Trustworthy AI:Securing sensitive data in large language models [J].arXiv:2409.18222,2024.
[57]STOCKS M.IBAC mathematics and mechanics:The case for‘integer based access control' of data security in the age of AI and AI automation [J].arXiv:2410.19021,2024.
[58]VIZGIRDA V,ZHAO R,GOEL N.SocialGenPod:Privacy-friendly generative AI social web applications with decentralised personal data stores [C]//Proceedings of the ACM Web Conference 2024 Companion.New York:ACM,2024:1174-1178.
[59]LONGPRE S,MAHARI R,CHEN A,et al.The Data Prove-nance Initiative:A large scale audit of dataset licensing & attribution in AI [J].arXiv:2310.16787,2023.
[60]XIE Y,SONG J,WANG H,et al.Training data provenance verification:Did your model use synthetic data from my generative model for training? [J].arXiv:2503.09122,2025.
[61]Coalition for Content Provenance and Authenticity.Content Authenticity Initiative [EB/OL].[2025-05-12].https://c2pa.org/.
[62]BELGODERE B M,DOGNIN P L,IVANKAY A,et al.Auditing and generating synthetic data with controllable trust trade-offs[J].IEEE Journal on Emerging and Selected Topics in Circuits and Systems,2023,14:773-788.
[63]MIT MEDIA LAB.Data provenance and consent in generative AI [EB/OL].(2024-12-31) [2025-05-12].https://mit-genai.pubpub.org/pub/uk7op8zs.
[64]ZHANG C Y,HU M W,LI W H,et al.Adversarial Attacks and Defenses on Text-to-Image Diffusion Models:A Survey[J].ar-Xiv:2407.15861,2024.
[65]GAO T,YAO X,CHEN D.SimCSE:SimpleContrastive Learning of Sentence Embeddings [C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2021:6894-6910.
[66]WU L,LI S,HSIEH C,et al.Stochastic shared embeddings:Data-driven regularization of embedding layers[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems.Red Hook,NY:Curran Associates Inc.,2019:24-33.
[67]YANG W L,YANG J,GUO Y,et al.Defensive Dual Masking for Robust Adversarial Defense[J].arXiv:2412.07078,2024.
[68]FRANCHI G,LAURENT O,LEGUERY M,et al.Make me a BNN:A simple strategy for estimating Bayesian uncertainty from pre-trained models[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE Computer Society,2024:12194-12204.
[69]XU X,LIU Z,KOFFAS S,et al.BAN:Detecting Backdoors Activated by Adversarial Neuron Noise[C]//Proceedings of the 38th Conference on Neural Information Processing Systems.Cambridge:MIT Press,2024:114348-114373.
[70]PAN X D,ZHANG M,SHENG B N,et al.Hidden trigger backdoor attack on NLP models via linguistic style manipulation[C]//Proceedings of the 31st USENIX Security Symposium.Berkeley:USENIX Association,2022:3611-3628.
[71]SUN H,ZHONG Q,QI M,et al.Towards robust speech mo-dels:Mitigating backdoor attacks via audio signal enhancement and fine-pruning techniques[J].Mathematics,2025,13(6):984.
[72]LIN W,LIU L,WEI S,et al.Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness[C]//Proceedings of the 38th Conference on Neural Information Processing Systems.Cambridge:MIT Press,2024:42097-42122.
[73]SUN Z,CONG T,LIU Y,et al.PEFTGuard:Detecting backdoor attacks against parameter-efficient fine-tuning [C]//Procee-dings of the 2025 IEEE Symposium on Security and Privacy.IEEE,2025:1713-1731.
[74]SHAH H,PARK S M,ILYAS A,et al.ModelDiff:A framework for comparing learning algorithms[C]//Proceedings of the 39th International Conference on Machine Learning.PMLR,2022:19482-19500.
[75]BAI Y,XING G,WU H,et al.Backdoor attack and defense on deep learning:A survey[J].IEEE Transactions on Computatio-nal Social Systems,2024,11(2):404-434.
[76]KIRSCHENBAUER J,GEIPING J,WEN Y,et al.A watermark for large language models [C]//Proceedings of the 40th International Conference on Machine Learning.PMLR,2023:17061-17084.
[77]DATHATHRI S,SEE A,GHAISAS S,et al.Scalable water-marking for identifying large language model outputs[J].Nature,2024,634(8035):818-823.
[78]REN H,XU H,LIU Y,et al.A Robust Semantics-Based Watermark for Large Language Models Against Paraphrasing[C]//Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.Singapore:Association for Computational Linguistics,2023:7199-7213.
[79]BALDASSIN F B,NGUYEN H H,CHANG C C,et al.Cross-Attention Watermarking of Large Language Models[C]//Proceedings of the 2024 IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2024:1-5.
[80]MA R,GUO M,LI Y,et al.PiGW:A plug-in generative watermarking framework [J].arXiv:2403.12053,2024.
[81]LI Z T,MENG X F,WANG L X,et al.A Survey on Machine Unlearning [J].Journal of Software,2025,36(4):1637-1664.
[82]JANG J,YOON D,YANG S,et al.Knowledge unlearning for mitigating privacy risks in language models[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics.Stroudsburg,PA:ACL,2023:14389-14408.
[83]CHEN J,YANG D.Unlearn what you want to forget:Efficient unlearning for LLMs[C]//Proceedings of the 28th Conference on Empirical Methods in Natural Language Processing.Stroudsburg,PA:ACL,2023:12041-12052.
[84]MAINI P,FENG Z,SCHWARZSCHILD A,et al.TOFU:ATask of Fictitious Unlearning forLLMs [J].arXiv:2401.06121,2024.
[85]ZHANG R,LIN L,BAI Y,et al.Negative Preference Optimization:From Catastrophic Collapse to Effective Unlearning [J].arXiv:2404.05868,2024.
[86]TIAN B,LIANG X,CHENG S,et al.To forget or not?Towards practical knowledge unlearning for large language models[C]//Proceedings of the 29th Conference on Empirical Methods in Natural Language Processing.Stroudsburg,PA:ACL,2024:1524-1537.
[87]MURESANU A,THUDI A,ZHANG M R,et al.Unlearnable Algorithms for In-context Learning [J].arXiv:2402.00751,2024.
[88]CHOI D,NA D.Distribution-Level Feature Distancing for Machine Unlearning:Towards a Better Trade-off Between Model Utility and Forgetting [C]//Proceedings of the AAAI Confe-renceon Artificial Intelligence.2025:2536-2544.
[89]WANG M R,YAO Y Z,XI Z K,et al.Security Analysis of Large Model Content Generation Based on Knowledge Editing [J].Journal of Computer Research and Development,2024,61(5):1143-1155.
[90]LIU C Y,WANG Y,FLANIGAN J,et al.Large Language Mo-del Unlearning via Embedding-Corrupted Prompts [J].arXiv:2406.07933,2024.
[91]WU X W,LI J Z,XU M H,et al.DEPN:Detecting and editing privacy neurons in pretrained language models[C]//Proceedings of the 28th Conference on Empirical Methods in Natural Language Processing.Stroudsburg,PA:ACL,2023:2875-2886.
[92]VENDITTI D,RUZZETTI E S,XOMPERO G A,et al.Enhancing Data Privacy in Large Language Models through Private Association Editing [J].arXiv:2406.18221,2024.
[93]LIU S,ZHENG C,WANG Y,et al.Unsupervised detection of adversarial examples with local outlier factors[J].IEEE Transactions on Information Forensics and Security,2024,19:5805-5818.
[94]WANG Y,LI H,HAN X,et al.Do-Not-Answer:A Dataset for Evaluating Safeguards in LLMs [J].arXiv:2308.13387,2023.
[95]ZHANG M,PAN X D,YANG M.JADE:A Linguistics-based Safety Evaluation Platform for Large Language Models[J].ar-Xiv:2308.07814,2023.
[96]LIANG P,BOMMASANI R,LEE T,et al.Holistic evaluation of language models[J/OL].Transactions on Machine Learning Research,2023.https://doi.org/10.48550/arXiv.2211.09110.
[97]HUANG R,WANG X,LIZ,et al.Guided Bench:Measuring and Mitigating the Evaluation Discrepancies of In-the-wild LLM Jailbreak Methods [J].arXiv:2502.,202516903.
[98]WANG Z,ZHANG G,YANG K,et al.Interactive Natural Language Processing [J].arXiv:2305.13246,2023.
[99]中国信息通信研究院,中国科学院计算技术研究所.大模型治理蓝皮报告:从规则走向实践[R].北京:中国信通院,2023-11-24.
[100]LEE K,KIM H,WHANG J J.SAIF:A comprehensive framework for evaluating the risks of generative AI in the public sector [J].arXiv:2501.08814,2025.
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