Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey Article Swipe
Shang Wang
,
Tianqing Zhu
,
Bo Liu
,
Ming Ding
,
Dayong Ye
,
Wanlei Zhou
,
Philip S. Yu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3764113
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3764113
With the rapid development of artificial intelligence, large language models (LLMs) have made remarkable advancements in natural language processing. These models are trained on vast datasets to exhibit powerful language understanding and generation capabilities across various applications, including chatbots and agents. However, LLMs have revealed a variety of privacy and security issues throughout their life cycle, drawing significant academic and industrial attention. Moreover, the risks faced by LLMs differ significantly from those encountered by traditional language models. Given that current surveys lack a clear taxonomy of unique threat models across diverse scenarios, we emphasize the unique privacy and security threats associated with four specific scenarios: pre-training, fine-tuning, deployment, and LLM-based agents. Addressing the characteristics of each risk, this survey outlines and analyzes potential countermeasures. Research on attack and defense situations can offer feasible research directions, enabling more areas to benefit from LLMs.
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Metadata
- Type
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- Language
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- Landing Page
- https://doi.org/10.1145/3764113
- https://dl.acm.org/doi/pdf/10.1145/3764113
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- References
- 75
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414128359Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3764113Digital Object Identifier
- Title
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Unique Security and Privacy Threats of Large Language Models: A Comprehensive SurveyWork title
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reviewOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-11Full publication date if available
- Authors
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Shang Wang, Tianqing Zhu, Bo Liu, Ming Ding, Dayong Ye, Wanlei Zhou, Philip S. YuList of authors in order
- Landing page
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https://doi.org/10.1145/3764113Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3764113Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3764113Direct OA link when available
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0Total citation count in OpenAlex
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75Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 15 |
| abstract_inverted_index.of | 4, 47, 85, 114 |
| abstract_inverted_index.on | 23, 125 |
| abstract_inverted_index.to | 26, 138 |
| abstract_inverted_index.we | 92 |
| abstract_inverted_index.and | 31, 39, 49, 59, 97, 108, 120, 127 |
| abstract_inverted_index.are | 21 |
| abstract_inverted_index.can | 130 |
| abstract_inverted_index.the | 1, 63, 94, 112 |
| abstract_inverted_index.LLMs | 42, 67 |
| abstract_inverted_index.With | 0 |
| abstract_inverted_index.each | 115 |
| abstract_inverted_index.four | 102 |
| abstract_inverted_index.from | 70, 140 |
| abstract_inverted_index.have | 11, 43 |
| abstract_inverted_index.lack | 81 |
| abstract_inverted_index.life | 54 |
| abstract_inverted_index.made | 12 |
| abstract_inverted_index.more | 136 |
| abstract_inverted_index.that | 78 |
| abstract_inverted_index.this | 117 |
| abstract_inverted_index.vast | 24 |
| abstract_inverted_index.with | 101 |
| abstract_inverted_index.Given | 77 |
| abstract_inverted_index.LLMs. | 141 |
| abstract_inverted_index.These | 19 |
| abstract_inverted_index.areas | 137 |
| abstract_inverted_index.clear | 83 |
| abstract_inverted_index.faced | 65 |
| abstract_inverted_index.large | 7 |
| abstract_inverted_index.offer | 131 |
| abstract_inverted_index.rapid | 2 |
| abstract_inverted_index.risk, | 116 |
| abstract_inverted_index.risks | 64 |
| abstract_inverted_index.their | 53 |
| abstract_inverted_index.those | 71 |
| abstract_inverted_index.(LLMs) | 10 |
| abstract_inverted_index.across | 34, 89 |
| abstract_inverted_index.attack | 126 |
| abstract_inverted_index.cycle, | 55 |
| abstract_inverted_index.differ | 68 |
| abstract_inverted_index.issues | 51 |
| abstract_inverted_index.models | 9, 20, 88 |
| abstract_inverted_index.survey | 118 |
| abstract_inverted_index.threat | 87 |
| abstract_inverted_index.unique | 86, 95 |
| abstract_inverted_index.agents. | 40, 110 |
| abstract_inverted_index.benefit | 139 |
| abstract_inverted_index.current | 79 |
| abstract_inverted_index.defense | 128 |
| abstract_inverted_index.diverse | 90 |
| abstract_inverted_index.drawing | 56 |
| abstract_inverted_index.exhibit | 27 |
| abstract_inverted_index.models. | 76 |
| abstract_inverted_index.natural | 16 |
| abstract_inverted_index.privacy | 48, 96 |
| abstract_inverted_index.surveys | 80 |
| abstract_inverted_index.threats | 99 |
| abstract_inverted_index.trained | 22 |
| abstract_inverted_index.variety | 46 |
| abstract_inverted_index.various | 35 |
| abstract_inverted_index.However, | 41 |
| abstract_inverted_index.Research | 124 |
| abstract_inverted_index.academic | 58 |
| abstract_inverted_index.analyzes | 121 |
| abstract_inverted_index.chatbots | 38 |
| abstract_inverted_index.datasets | 25 |
| abstract_inverted_index.enabling | 135 |
| abstract_inverted_index.feasible | 132 |
| abstract_inverted_index.language | 8, 17, 29, 75 |
| abstract_inverted_index.outlines | 119 |
| abstract_inverted_index.powerful | 28 |
| abstract_inverted_index.research | 133 |
| abstract_inverted_index.revealed | 44 |
| abstract_inverted_index.security | 50, 98 |
| abstract_inverted_index.specific | 103 |
| abstract_inverted_index.taxonomy | 84 |
| abstract_inverted_index.LLM-based | 109 |
| abstract_inverted_index.Moreover, | 62 |
| abstract_inverted_index.emphasize | 93 |
| abstract_inverted_index.including | 37 |
| abstract_inverted_index.potential | 122 |
| abstract_inverted_index.Addressing | 111 |
| abstract_inverted_index.artificial | 5 |
| abstract_inverted_index.associated | 100 |
| abstract_inverted_index.attention. | 61 |
| abstract_inverted_index.generation | 32 |
| abstract_inverted_index.industrial | 60 |
| abstract_inverted_index.remarkable | 13 |
| abstract_inverted_index.scenarios, | 91 |
| abstract_inverted_index.scenarios: | 104 |
| abstract_inverted_index.situations | 129 |
| abstract_inverted_index.throughout | 52 |
| abstract_inverted_index.deployment, | 107 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.directions, | 134 |
| abstract_inverted_index.encountered | 72 |
| abstract_inverted_index.processing. | 18 |
| abstract_inverted_index.significant | 57 |
| abstract_inverted_index.traditional | 74 |
| abstract_inverted_index.advancements | 14 |
| abstract_inverted_index.capabilities | 33 |
| abstract_inverted_index.fine-tuning, | 106 |
| abstract_inverted_index.applications, | 36 |
| abstract_inverted_index.intelligence, | 6 |
| abstract_inverted_index.pre-training, | 105 |
| abstract_inverted_index.significantly | 69 |
| abstract_inverted_index.understanding | 30 |
| abstract_inverted_index.characteristics | 113 |
| abstract_inverted_index.countermeasures. | 123 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.18613142 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |