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fix: llm04 data poisoning links #747

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Nov 10, 2025
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fix: llm04 data poisoning links #747

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12 changes: 6 additions & 6 deletions 2_0_vulns/LLM04_DataModelPoisoning.md
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Expand Up @@ -11,12 +11,12 @@ Moreover, models distributed through shared repositories or open-source platform
### Common Examples of Vulnerability

1. Malicious actors introduce harmful data during training, leading to biased outputs. Techniques like "Split-View Data Poisoning" or "Frontrunning Poisoning" exploit model training dynamics to achieve this.
(Ref. link: [Split-View Data Poisoning](https://github.com/GangGreenTemperTatum/speaking/blob/main/dc604/hacker-summer-camp-23/Ads%20_%20Poisoning%20Web%20Training%20Datasets%20_%20Flow%20Diagram%20-%20Exploit%201%20Split-View%20Data%20Poisoning.jpeg))
(Ref. link: [Frontrunning Poisoning](https://github.com/GangGreenTemperTatum/speaking/blob/main/dc604/hacker-summer-camp-23/Ads%20_%20Poisoning%20Web%20Training%20Datasets%20_%20Flow%20Diagram%20-%20Exploit%202%20Frontrunning%20Data%20Poisoning.jpeg))
2. Attackers can inject harmful content directly into the training process, compromising the model’s output quality.
3. Users unknowingly inject sensitive or proprietary information during interactions, which could be exposed in subsequent outputs.
4. Unverified training data increases the risk of biased or erroneous outputs.
5. Lack of resource access restrictions may allow the ingestion of unsafe data, resulting in biased outputs.
(Ref. link: [Split-View Data Poisoning](https://github.com/GangGreenTemperTatum/speaking/blob/aad68f8521119596abb567d94fbd10bdd652ac82/docs/conferences/dc604/hacker-summer-camp-23/Ads%20_%20Poisoning%20Web%20Training%20Datasets%20_%20Flow%20Diagram%20-%20Exploit%201%20Split-View%20Data%20Poisoning.jpeg))
(Ref. link: [Frontrunning Poisoning](https://github.com/GangGreenTemperTatum/speaking/blob/aad68f8521119596abb567d94fbd10bdd652ac82/docs/conferences/dc604/hacker-summer-camp-23/Ads%20_%20Poisoning%20Web%20Training%20Datasets%20_%20Flow%20Diagram%20-%20Exploit%202%20Frontrunning%20Data%20Poisoning.jpeg))
1. Attackers can inject harmful content directly into the training process, compromising the model’s output quality.
2. Users unknowingly inject sensitive or proprietary information during interactions, which could be exposed in subsequent outputs.
3. Unverified training data increases the risk of biased or erroneous outputs.
4. Lack of resource access restrictions may allow the ingestion of unsafe data, resulting in biased outputs.

### Prevention and Mitigation Strategies

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