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Open nowPosted 1060 days ago

大模型智能体安全研究员-智能安全

ByteDance19,210 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now大模型智能体安全研究员-智能安全ByteDance · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 2 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 1060 days ago

ByteDance median: 2 days open

The posting

1、智能体安全风险发现、建模与评估：针对大模型智能体系统（含工具调用、插件集成、多步工作流编排等场景），系统性地发现和建模安全风险，包括但不限于Prompt Injection（直接、间接注入）、指令绕过、越权调用外部工具或第三方服务、敏感数据泄露、上下文污染、模型幻觉与推理偏差引发的业务风险等；建立风险分级与影响评估框架，制定安全基线标准与准入规范； 2、防护策略与安全机制设计：结合威胁模型与业务场景，设计并迭代智能体系统的安全防护架构与策略规则体系，涵盖输入输出过滤、Prompt防护层、工具调用权限沙箱、上下文隔离、敏感操作审批、安全对齐（Alignment）策略以及基于意图识别的异常行为检测等方向； 3、安全评测体系建设与红队测试：构建覆盖大模型智能体核心攻击面的标准化安全评测基准（Benchmark），组织和执行红队测试（Red Teaming），开发自动化攻击模拟工具与对抗性测试框架，持续验证并推动防护能力的工程化与规模化； 4、前沿安全研究与情报跟踪：持续跟踪AI安全领域的最新学术研究、攻击技术演进与行业安全事件，提炼可落地的防护思路与方法论，保持团队在大模型安全攻防方面的技术前瞻性； 5、跨团队协作与安全能力落地：与产品、工程、算法及合规团队紧密协作，将安全研究成果转化为可工程化的安全组件和规范流程，推动安全能力在智能体产品全链路中的系统性集成与持续运营。

1、专业背景：计算机科学、信息安全、人工智能、网络安全或相关领域本科及以上学历，或具备同等水平的实际工作经验与技术能力； 2、大模型技术理解与AI安全知识储备：熟悉大语言模型（LLM）的基本原理与推理机制，了解主流Agent框架及其工作模式（如基于工具调用的ReAct/Function Calling范式、多步工作流编排、插件系统、RAG架构等）；了解当前主流的AI安全问题与攻防方法，包括但不限于Prompt Injection（直接注入与间接注入）、Jailbreak、数据投毒、模型逆向、对抗样本、模型滥用及越权调用第三方服务等攻击手段与防御思路； 3、安全攻防或威胁建模经验：具备一定的安全研究、渗透测试、红队演练或威胁建模经验，能够从攻击者视角系统性地分析复杂系统的安全风险面； 4、代码实现能力：具备扎实的编程能力（Python为主，其他语言加分），能独立构建概念验证（POC）、安全测试脚本、自动化评估工具或对抗性测试框架； 5、逻辑抽象、分析能力与风险意识：具有优秀的问题抽象能力和结构化思维，能够将模糊的、新兴的安全风险转化为清晰的威胁模型、评估指标和可执行的防护方案；对新技术、新场景保持敏锐的安全感知，能够主动识别潜在风险点，而非仅被动响应已知漏洞； 6、跨团队沟通与协作：具备良好的书面和口头表达能力，能够清晰地向非安全背景的技术和业务团队传达安全风险与建议，推动安全方案的落地执行。

加分项 1、实际LLM/Agent安全经验：有大模型应用或智能体系统的安全研究、攻防测试或安全治理的实战经验，曾主导或深度参与过相关安全项目； 2、学术成果或开源贡献：在AI安全、对抗机器学习、系统安全等领域发表过高质量学术论文（如顶会S&P、USENIX Security、CCS、NeurIPS、ICML、ACL等），或在相关开源项目中有持续贡献； 3、安全竞赛成绩：在CTF、AI安全挑战赛、红队竞赛或漏洞挖掘项目中取得优异成绩，具备竞技型安全攻防实力； 4、安全工程化能力：有将安全研究成果转化为自动化工具、平台化能力或规模化服务的工程经验； 5、多领域交叉背景：同时具备传统网络安全/应用安全与AI/NLP技术背景，能够将经典安全思维与大模型场景有机结合； 6、行业影响力：在AI安全社区有一定影响力，如维护技术博客、参与行业标准制定、在安全会议上发表演讲等。

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