大模型安全风险概述

大模型在广泛应用的同时，面临数据泄露与 prompt 注入两大核心风险。数据泄露指模型生成内容包含训练数据中的敏感信息；prompt 注入则是通过恶意输入操纵模型输出，导致越权行为或隐私泄露。

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数据泄露防护方案

数据脱敏与匿名化
在训练阶段对原始数据进行脱敏处理，例如替换真实姓名、地址等敏感信息为匿名标识符。采用差分隐私技术，在数据中注入可控噪声，降低模型记忆特定数据的能力。

模型微调与过滤
通过微调模型避免生成敏感内容，例如使用 RLHF（强化学习人类反馈）优化输出安全性。部署后置过滤器，实时检测并拦截包含敏感信息的生成内容。

访问控制与日志审计
严格限制模型访问权限，仅允许授权用户调用 API。记录所有查询日志，定期审计异常行为（如高频查询、特定关键词触发）。

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prompt 注入防护方案

输入 sanitization
对用户输入进行预处理，移除或转义特殊字符（如 <>{}）和恶意代码片段。采用白名单机制，仅允许合规的输入格式通过。

上下文隔离与沙盒执行
为每次对话分配独立上下文，避免攻击者通过历史输入影响后续输出。在沙盒环境中运行模型，限制其对系统资源的访问权限。

对抗性训练
在训练阶段加入对抗样本，增强模型对恶意 prompt 的鲁棒性。例如，模拟攻击者构造的注入指令（如“忽略之前指令”），训练模型识别并拒绝执行。

动态检测与响应
实时监控模型输出，触发异常时自动终止会话。例如，检测到越权操作（如数据库查询请求）时，立即阻断并告警。

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综合防护框架

• 分层防御：结合预处理、模型层、后处理三层防护，覆盖数据全生命周期。
• 持续更新：定期评估新攻击模式，更新防护策略与规则库。
• 合规性：遵循 GDPR、HIPAA 等法规，确保数据处理与模型行为合法。

通过上述方案，可系统性降低大模型在数据泄露与 prompt 注入方面的风险，平衡功能性与安全性需求。

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