By Sarang Warudkar - Sr. CASB Technical Product Marketing Manager, Skyhigh Security
December 16, 2024 3 Minute Read
The rapid adoption of Large Language Models (LLMs) has revolutionized organizations’ use of AI, from improving customer interactions to enabling advanced data analysis. However, with AI’s power comes the responsibility to address emerging security vulnerabilities. Simple prompt-level protections are no longer enough—what’s needed is a strong, system-wide approach to secure AI pipelines effectively.
Skyhigh SSE, a pioneer in AI security, delivers a comprehensive framework aligned with the 2025 OWASP Top 10 LLM threats, ensuring AI systems remain safe, ethical, and reliable. Let’s explore the key risks and how Skyhigh SSE mitigates them.
A System-Level Approach to AI Security
The OWASP Top 10 LLM threats provide a clear roadmap for securing LLM applications, emphasizing visibility and controls across every stage of the AI system—from inputs and outputs to model training and supply chains.
OWASP Top 10 LLM Threats and Mitigation Using Skyhigh SSE
1. Prompt Injection
Prompt injection manipulates LLM behavior through malicious inputs, compromising outputs and sensitive data.
Mitigation using Skyhigh SSE:
- Allow access only to trusted AI services through Shadow IT and URL filtering.
- Block harmful prompts with DLP policies and input/output filtering.
- Secure AI interactions using Remote Browser Isolation (RBI).
- Validate outputs to prevent manipulation.
2. Sensitive Information Disclosure
LLMs can unintentionally expose PII, financial data, or business-critical information.
Mitigation using Skyhigh SSE:
- Data sanitization to mask sensitive inputs.
- DLP policies to detect and block confidential data leaks.
- Implement least-privilege access to restrict data exposure.
3. Supply Chain Vulnerabilities
Third-party models, datasets, or plugins may introduce backdoors, biases, or security flaws.
Mitigation using Skyhigh SSE:
- Restrict usage to verified providers with Shadow IT management.
- Audit external components for integrity and risk.
- Enforce sanitization across datasets, plugins, and models.
4. Data and Model Poisoning
Attackers inject malicious data during training, corrupting model behavior.
Mitigation using Skyhigh SSE:
- Apply DLP filtering to block harmful content.
- Validate data sources to ensure reliability.
- Detect anomalies with risky behavior monitoring tools like Risky prompt detection.
5. Improper Output Handling
Unvalidated outputs may expose sensitive data or enable risks like SQL injection.
Mitigation using Skyhigh SSE:
- Enforce response firewalls to validate outputs.
- Apply Zero Trust policies for strict output controls.
- Prevent exposure using RBI.
6. Excessive Agency
LLMs with excessive privileges can interact with unauthorized systems or data.
Mitigation:
- Limit functionality with entitlement controls.
- Ensure only approved prompts interact with systems.
- Use cross-verification to restrict overreach.
7. System Prompt Leakage
Attackers exploit system-level instructions to access sensitive configurations.
Mitigation using Skyhigh SSE:
- Use DLP policies to block access to system prompts.
- Isolate critical configurations.
- Filter outputs to prevent leaks.
8. Vector and Embedding Weaknesses
Insecure embeddings and vector databases can be manipulated for unauthorized access.
Mitigation:
- Enforce entitlement controls for secure access.
- Validate and sanitize vectors.
- Continuously monitor for anomalies.
Mitigation using Skyhigh SSE:
- Detect anomalies with risky behavior monitoring tools like Risky prompt detection.
9. Misinformation
LLMs may generate inaccurate or misleading outputs (hallucinations), harming reliability.
Mitigation:
- Connect LLMs to trusted RAG frameworks.
- Cross-verify outputs using automated tools and human oversight.
- Monitor for inconsistencies and correct misinformation.
10. Unbounded Consumption
Excessive queries can overwhelm systems, leading to resource exhaustion.
Mitigation using Skyhigh SSE:
- Enforce rate limiting and throttling to maintain stability.
- Restrict query sizes through input validation.
- Apply QoS policies to optimize resource allocation.
The Path Forward: Comprehensive AI Security with Skyhigh SSE
The interconnected, data-driven nature of modern AI systems demands a proactive, system-level security approach. Skyhigh SSE leads the way by addressing the full spectrum of OWASP Top 10 vulnerabilities.
By mitigating risks such as prompt injection, sensitive data leaks, and excessive agency, Skyhigh SSE enables organizations to secure their AI applications while maintaining performance and scalability.
Securing the Future of AI
Skyhigh SSE empowers businesses to unlock the full potential of AI safely and ethically. With its advanced security tools and multi-layered protections, Skyhigh SSE ensures that AI deployments remain trustworthy, scalable, and compliant.
Together, we can secure the future of AI.
Are you ready to secure your organization’s AI journey? Click here to learn more about how Skyhigh Security is safeguarding AI applications and leading the way in data protection for the AI era.
Back to Blogs