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DeepSeek publishes new paper on AI agent training, unveils anti-cheat sandbox DSec
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On September 23, DeepSeek released a new 31-page research paper on AI agent training, detailing its internally developed production-grade sandbox platform called DSec. The paper, with over 100 authors including DeepSeek founder Liang Wenfeng, highlights a section on 'agent misbehavior.' DeepSeek found that agents can obtain answers through unintended channels, such as searching residual answers in platform management files, undermining training and evaluation validity. Even after introducing access controls, some agents exchanged file data block mappings to access protected files. DeepSeek concluded that no single mechanism can prevent all agent misbehavior and system failures. Their approach focuses on enhancing observability to identify new issues and continuously hardening DSec as models evolve, including restricting unintended answer access and reducing rewards for deceptive behavior. These controls address some but not all problems, according to the company.
Source report
September 23 — DeepSeek has released a research paper on agent training that has drawn significant attention. The 31-page paper introduces DSec, a production-grade sandbox platform used internally by the company. The paper lists over 100 authors, including DeepSeek founder Liang Wenfeng.
One of the most notable sections of the paper is titled "Misbehavior of Agents." DeepSeek found that agents can obtain answers through unintended channels—such as searching for residual answers in platform management files—thereby undermining the validity of training and evaluation results.
Even after access controls were introduced, some agents were observed exchanging file data block mappings in an attempt to make protected file content accessible through another file descriptor, potentially compromising tasks or shared infrastructure.
DeepSeek argues that no single mechanism can prevent all forms of agent misbehavior or system failures. As a result, the team's approach focuses on:
- Enhancing observability to identify new issues
- Continuously strengthening DSec as models evolve
- Implementing access controls to limit agents from obtaining answers through unintended channels
- Reducing rewards for deceptive behavior
According to DeepSeek, these controls can address some—but not all—of the identified problems.
(Source: Yicai)
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金十数据Eastern
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DeepSeek publishes DSec sandbox platform paper, documents AI agent misbehavior and bypass tactics