A dataset used to train large language models (LLMs) has been found to contain nearly 12,000 live secrets, which allow for successful authentication. The findings once again highlight how hard-coded c...
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Discussion Points
- The use of hard-coded credentials in dataset training raises significant concerns about user security and organizational risks.r
- Large language models' tendency to suggest insecure coding practices exacerbates the issue, potentially putting users at greater risk.r
- The discovery highlights the need for improved data protection measures and robust security protocols.
Summary
The recent discovery of nearly 12,000 live secrets in a dataset used to train large language models (LLMs) is a stark reminder of the severe security risks associated with hard-coded credentials. These credentials allow for successful authentication, putting users and organizations at significant risk.r This issue is compounded when LLMs suggest insecure coding practices to their users, further perpetuating the problem.
The fact that this dataset was used in training these models highlights the need for improved data protection measures and robust security protocols.r The consequences of such vulnerabilities can be devastating, emphasizing the importance of prioritizing security and taking proactive measures to mitigate these risks.