2025放眼算法之外_AI_真实风险报告_96页_16mb
报告摘要
Summary of "Beyond Algorithms: The Real Risks of AI"
Core Content
This report provides a comprehensive analysis of the technical, operational, and human risks associated with the adoption of AI in organisations. It highlights the growing complexity of AI security, emphasizing the need for a holistic approach that integrates technological innovation with robust risk management and algorithmic accountability.
The report is structured around three key levels:
- Technical Risks: Focus on vulnerabilities in AI models, data, and infrastructure.
- Operational Risks: Address the impact of AI on internal processes, business continuity, and organisational resilience.
- Human Risks: Consider the role of error, ignorance, and manipulation in AI security.
It also identifies the actors who exploit these risks, including cybercriminals and state-sponsored groups, and provides practical recommendations to mitigate these threats while aligning with European regulatory frameworks such as the AI Act, NIS2, ENS, and DORA.
Main Attack Types and Risk Vectors
| Attack Type | Affected Phase | Main Impact | Related to... |
|---|---|---|---|
| Prompt Injection | Input / Integrations | Integrity / Confidentiality | Insecure Output, Insecure Plugins, Excessive Agency |
| Insecure Output Management | Inference / Operation | Integrity / Physical Security / Cost | Prompt Injection, Poisoning, Bias, Excessive Dependency |
| Data Poisoning | Data / Training | Model Integrity | Supply Chain, Insecure Output |
| Data Leakage | Training / Inference | Confidentiality / Compliance | Disclosure of Confidential Information, Excessive Dependency |
| Denial of Service (DoS) | Operation / Integrations | Availability / Cost | Supply Chain, Excessive Agency |
| Supply Chain Vulnerabilities | Data / Training / Operation | Integrity / Confidentiality / Availability | Poisoning, Insecure Output, Insecure Plugins |
| Disclosure of Confidential Information | Integrations / Inference | Confidentiality / Reputation | Data Leakage, Insecure Output, Excessive Dependency |
| Insecure Plugins | Integrations / Operation | Integrity / Confidentiality | Insecure Output, Excessive Agency, Excessive Dependency |
| Excessive Agency | Operation / Business Logic | Systemic Risk | Prompt Injection, Insecure Output, Plugins, Excessive Dependency |
| Excessive Dependency | Governance / Operation | Continuity / Sovereignty | Excessive Agency, Insecure Output, Disclosure |
| Model Theft | Training / Inference | I.P. / Evasion | Data Leakage, Poisoning, Excessive Dependency |
Key Risks and Impacts
- Prompt Injection: Manipulates model inputs to alter behavior or extract sensitive data. Can bypass traditional security controls.
- Insecure Output Management: Outputs are used in critical processes without validation, leading to errors, fraud, or compliance issues.
- Data Poisoning: Introduces harmful data during training, affecting model integrity and potentially enabling attacks.
- Data Leakage: Models may inadvertently expose confidential or private data through queries, attacks, or configuration errors.
- Denial of Service (DoS): Overwhelms AI models with traffic or manipulated data, affecting availability and increasing costs.
- Supply Chain Vulnerabilities: Third-party components introduce inherited risks, potentially compromising the entire AI pipeline.
- Excessive Agency: AI systems perform actions autonomously without sufficient human oversight, amplifying existing vulnerabilities.
- Excessive Dependency: Over-reliance on AI reduces human control and increases exposure to external threats.
These risks not only threaten the technical integrity of AI systems but also have operational and legal consequences, especially in regulated sectors such as healthcare, finance, and public administration.
Actors Exploiting AI Risks
- Cybercrime Groups: Such as FunkSec, GXC Team, Indrik Spider, Renaissance Spider.
- APT Groups: Including APT28, Ember Bear, APT41, RedHotel, Sodium, Ta499, Imperial Kitten, Charming Kitten, APT42, Lazarus Group, Void Arachne.
These actors are increasingly using AI to enhance their capabilities, including Malware as a Service, Ransomware as a Service, and AI-based attacks, which require advanced defences and awareness.
Recommendations
- AI Security Audit: Regularly assess AI systems for vulnerabilities, using adversarial prompt libraries and frameworks like the OWASP Top 10 for LLM Applications.
- Awareness and Cybersecurity Training: Educate administrators and security teams to identify manipulation patterns and anomalous behavior.
- Input and Output Controls: Implement strict input filtering, sandbox environments, and access restrictions to prevent injection and manipulation.
- Data Governance: Validate data quality, apply privacy-enhancing technologies (PETs), and ensure traceability and version control in data pipelines.
- Supply Chain Management: Audit and patch external dependencies, maintain SBOM practices, and ensure secure integration with third-party services.
- Human Oversight: Ensure human validation in critical decisions, especially in high-impact environments, to prevent over-agency and over-dependency.
- Regulatory Compliance: Align with the AI Act, GDPR, NIS2, and ENS to ensure ethical, secure, and traceable AI deployment.
About S2GRUPO
S2GRUPO is a company that provides informational and professional insights on AI security. The report reflects their expert analysis and aims to support CISOs, technology managers, and cybersecurity teams in understanding and managing AI-related risks.
Conclusion
The adoption of AI introduces new and complex security challenges that require a multi-layered approach to mitigate. By addressing technical, operational, and human risks, and by understanding the actors and attack vectors, organisations can better prepare for emerging threats and ensure safe, ethical, and sustainable AI deployment.
试读结束，高清完整版pdf/doc/ppt,请点下载