Browse AI risks

9 entries

1. 74.01.01 · Risk Sub-Category

   Inherent Risk

   Privacy - Membership Inference Attack (MIA)

   "inferring whether a given text record is used for training LLM"

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Post-deployment

2. 74.01.02 · Risk Sub-Category

   Inherent Risk

   Privacy - Data Extraction Attack (DEA)

   "extracting the text records that exist in the training dataset"

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Post-deployment

3. 74.01.03 · Risk Sub-Category

   Inherent Risk

   Privacy - Prompt Inversion Attack (PIA)

   "stealing the private prompting texts"

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Post-deployment

4. 74.01.04 · Risk Sub-Category

   Inherent Risk

   Privacy - Attribute Inference Attack (AIA)

   "deducing the private or sensitive information from training texts, prompting texts or external texts"

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Post-deployment

5. 74.01.05 · Risk Sub-Category

   Inherent Risk

   Privacy - Model Extraction Attack (MEA)

   "replicating the parameters of the LLM,"

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Post-deployment

6. 74.02.02 · Risk Sub-Category

   Malicious Use

   Jailbreak in LLM Malicious Use - Poisoning Training Data

   "In the data collecting and pre-training phase, malicious adversaries can Jailbreak LLMs through poisoning their training data to make the model to output harmful content."

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Pre-deployment

7. 74.02.03 · Risk Sub-Category

   Malicious Use

   Jailbreak in LLM Malicious Use - Backdoor Attack

   "However, there are still ones who can leave holes in the training dataset, making LLMs appear safe on average, but generate harmful content under other specific conditions. This kind of attack can be categorized as "backdoor attack". Evan et al. developed a backdoor model that behaves as expected when trained, but exhibits different and potentially harmful behavior when deployed [81]. The results show that these backdoor behaviors persist even after multiple security training techniques are applied."

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Pre-deployment

8. 74.02.04 · Risk Sub-Category

   Malicious Use

   Jailbreak in LLM Malicious Use - White & Black Box Attacks

   "In the fine-tuning and alignment phase, elaborately- designed instruction datasets can be utilized to fine-tune LLMs to drive them to perform undesirable behaviors, such as generating harmful information or content that violates ethical norms, and thus achieve a jailbreak. Based on the accessibility to the model parameters, we can categorize them into white-box and black-box attacks. For white-box attacks, we can jailbreak the model by modifying its parameter weights. In [107], Lermen et al. used LoRA to fine-tune the Llama2’s 7B, 13B, and 70B as well as Mixtral on AdvBench and RefusalBench d

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Pre-deployment

9. 74.02.05 · Risk Sub-Category

   Malicious Use

   Jailbreak in LLM Malicious Use - Prompt Attacks

   "In the prompting and reasoning phase, dialog can push LLMs into confused or overly compliant states, raising the risk of producing harmful outputs when confronted with harmful questions. Most of the jailbreak methods in this phase are black-boxed and can be categorized into four main groups based on the type of method: Prompt Injection [154], Role Play, Adversarial Prompting, and Prompt Form Transformation."

   From A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)

   Domain 2 2.2 Human Intentional Post-deployment

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