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Open nowPosted 190 days ago

大模型与智能体安全——面向Agentic AI的攻防评估与可信安全防御（LLM Security）-安全与风控（北京/深圳）

ByteDance19,258 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now大模型与智能体安全——面向Agentic AI的攻防评估与可信安全防御（LLM Security）-安全与风控（北京/深圳）ByteDance · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

11.3% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.3%7 days
  4. 17.6%14 days
  5. 34.3%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 23 days open

The posting

团队介绍：我们团队为字节跳动的大型语言模型（LLM）业务线提供端到端安全解决方案。 我们针对模型训练、推理及部署全流程识别并防控新型风险，同时研发工具与系统，保障 LLM 资产及相关应用的安全。 通过在实际业务场景中推进人工智能安全技术落地，构建了行业领先的防御体系，为人工智能技术的安全演进提供保障。

课题介绍： 智能体应用在爆发式增长同时，也引入了新的安全挑战。包括数据、决策、执行、供应链在内的更大攻击面，及其错综的交互特点和复杂的基础设施，使得传统安全技术纷纷失效，智能体、数据、模型等资产面临着失控、失窃和受损的风险。 本课题旨在系统研究Agentic AI时代下大模型与智能体的攻防测试评估技术，以及基于大模型可信隐私计算的全链路防御体系，保障公司大模型业务的安全平稳发展，包括： 1、多模态大模型与智能体的攻防检测与评估方法、测试基准以及工具链； 2、面向智能体应用的风险防御技术以及可信安全运行保障技术； 3、大模型以及海量样本下的训练与推理技术，保障数据与模型资产安全； 4、面向大模型的机密训推基础设施安全构建、性能优化以及国产卡适配。

课题挑战： 1、智能体高权限下提示注入、记忆投毒等新攻击难以检测，多模态为对抗样本攻击提供了新路径； 2、插件生态缺乏审计，供应链投毒规模蔓延，如何为智能体构建过程完整、数据机密、行为可信的防护体系； 3、在大模型上百B的参数以及海量样本场景下，训练与推理如何实现安全、性能与成本的均衡，可信、可运维、高性能的工业级机密大模型训推基础框架如何构建。

课题价值： 1、技术价值： 1.1构建Agentic AI多模态安全评估基准与红队测试工具链； 1.2构建领先的大模型安全训练与推理算法、系统以及智能体安全可信防护能力。 2、业务价值： 2.1为智能体产品提供运行时安全防护能力以及可信可证的用户隐私保护措施，保障Flow等大模型和智能体产品安全运行； 2.2面向第三方算力环境提供安全可信的大模型数据与资产保护能力。

1、2027届毕业，获得博士学位，人工智能、计算机、数学相关专业优先； 2、优秀的代码能力，有扎实的机器学习基础，在ICML/NeurIPS/ICLR等顶级会议上发表论文者优先； 3、具有个人维护的相关开源项目的同学优先； 4、出色的问题分析和解决能力，有自主探索解决方案的能力； 5、良好的沟通协作能力，能和团队一起探索新技术，推进技术进步； 6、出色的心理素质与应变能力，面对困难勇于迎接挑战；面对复杂局面沉着、冷静、灵活。

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