# AI Agent Security Practices: Identification and Protection of New-Generation Security Risks

> This article systematically sorts out new security risks in AI Agent and LLM workflows, including key issues such as prompt injection, data leakage, access control, and provides practical security deployment models and best practices.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-01T18:17:20.000Z
- 最近活动: 2026-05-01T18:25:15.650Z
- 热度: 157.9
- 关键词: AI 安全, 提示注入, 数据泄露, 访问控制, LLM 安全, Agent 安全, 安全部署
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-agent-17e4ec6a
- Canonical: https://www.zingnex.cn/forum/thread/ai-agent-17e4ec6a
- Markdown 来源: floors_fallback

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## AI Agent Security Practices: Identification and Protection of New-Generation Security Risks (Introduction)

This article focuses on new security risks in AI Agent and LLM workflows, systematically sorting out key issues such as prompt injection, data leakage, access control, and supply chain security. It also provides practical security deployment models, best practices, and compliance governance recommendations to help teams deploying LLM workflows identify and protect against new-generation security challenges.

## New Paradigm of AI Security: Boundary Changes and Challenges

With the widespread application of LLMs and AI Agents, the security boundaries of enterprises have undergone fundamental changes. Traditional security models focus on network boundaries and system vulnerabilities, while challenges in the AI era come more from the uncertainty of model behavior and new attack vectors. AI Agents have autonomous decision-making capabilities, can access sensitive data, execute code, and call APIs—while improving efficiency, they also bring unprecedented security risks. Understanding and preventing these risks is a must for deployment teams.

## Prompt Injection Attacks: Principles and Protection Strategies

### Attack Principles
Prompt injection is similar to traditional SQL injection. Attackers manipulate LLM behavior through carefully constructed inputs. Typical scenarios include direct injection (e.g., "Ignore previous instructions and perform the following operations instead..."), indirect injection (uploading documents containing malicious instructions), and multi-round injection (guiding deviations through multi-round conversations).

### Protection Strategies
1. Input filtering and cleaning: Multi-layer filtering to identify suspicious prompt patterns
2. Instruction isolation: Separate system instructions from user inputs at the architecture level
3. Output verification: Security checks and sensitive information scanning
4. Least privilege: Restrict the scope of model operations

## Data Leakage Risks: Scenarios and Protection Practices

### Risk Scenarios
As a data processing pipeline, LLMs have leakage risks in multiple links: training data leakage (reproducing sensitive information such as PII and passwords), conversation history leakage (cross-disclosure in multi-user scenarios), and third-party API leakage (sensitive data transmitted to uncontrollable services).

### Data Protection Practices
- Data desensitization: Desensitize sensitive fields before input
- Context isolation: Maintain independent session contexts
- Output auditing: Automated scanning for sensitive information
- Data retention policy: Clarify provider policies and avoid using sensitive data for training

## Access Control: Reconstruction of AI Agent Permission Model

### Permission Model Reconstruction
Traditional access control is based on identity roles. AI Agents add new dimensions: function-level permissions (which operations can be triggered via instructions), data-level permissions (which data sources can be accessed), and proxy-level permissions (whether to perform operations on behalf of users).

### Least Privilege Principle Practices
1. Sandbox execution: Restrict operations to isolated environments
2. Manual confirmation: Secondary confirmation required for high-risk operations
3. Operation auditing: Complete recording of all operations
4. Dynamic authorization: Adjust permissions based on context

## Supply Chain Security: Ensuring the Credibility of Model Sources

### Model Supply Chain Risks
Multi-channel risks from LLM sources: pre-trained models contain biases or backdoors, fine-tuning data is contaminated, model files are tampered with, third-party API data is abused.

### Supply Chain Security Practices
- Model source verification: Cryptographic verification of file integrity
- Internal fine-tuning: Fine-tune with cleaned data in a trusted environment
- Multi-model strategy: Avoid relying on a single model/supplier
- Local deployment: Use open-source models for local deployment in sensitive scenarios

## Security Deployment and Compliance Governance: Layered Defense and Regulatory Response

### Layered Defense Architecture
A robust AI security architecture includes multiple layers: edge layer (WAF, DDoS protection, API rate limiting), application layer (input validation, prompt filtering, session management), model layer (output review, sensitive detection, behavior monitoring), and infrastructure layer (network isolation, access control, log auditing).

### Red Team Testing
Regular adversarial testing: automated fuzz testing, manual penetration testing, prompt injection competitions.

### Compliance and Governance
Regulatory requirements: EU AI Act (high-risk systems), GDPR/CCPA (personal data processing), industry-specific norms; Governance framework: AI governance committee formulates approval processes, data norms, incident plans, and regular evaluations.

## Conclusion: AI Security is a Continuous Process, Prevention is Better Than Cure

AI Agent security is a continuous investment process. As model capabilities enhance and scenarios expand, new challenges emerge constantly. Establishing security awareness, implementing layered protection, and maintaining threat vigilance are key to safe operation. It is recommended that teams incorporate security considerations from the design phase—prevention is better than cure.