Full Report
As generative AI and large language models (LLMs) are embedded into a greater number of internal processes and customer-facing applications, the risks associated with LLMs are growing.
Analysis Summary
As a vulnerability research specialist, I have summarized the provided context, which details the OWASP Top 10 Risks for LLM Applications (2025). Since this is a generalized risk list rather than a specific patchable software vulnerability, CVEs and specific product versions are not applicable. The summary focuses on the identified classes of risks and their mitigations.
# Vulnerability: OWASP Top 10 Risks for Large Language Models (2025)
## CVE Details
- CVE ID: N/A (This outlines classes of risks, not specific CVEs)
- CVSS Score: N/A
- CWE: Various, including Injection, Sensitive Information Disclosure, Supply Chain, Integrity, and Availability related CWEs.
## Affected Systems
- Products: All applications and systems implementing/integrating Large Language Models (LLMs) and Generative AI solutions.
- Versions: All versions currently integrating unmitigated LLMs.
- Configurations: Any system where user input directly or indirectly influences model behavior or where confidential data interacts with the model's pipeline.
## Vulnerability Description
The OWASP Top 10 for LLM Applications (2025) outlines ten primary security risks associated with embedding generative AI and LLMs into applications. These risks cover input manipulation (Prompt Injection), unauthorized data exposure (Sensitive Information Disclosure), third-party risks (Supply Chain, Poisoning), model manipulation (Model Denial of Service, Training Data Poisoning), output integrity issues (Hallucinations, Insecure Output Handling), and resource abuse (Unbounded Consumption). These issues can lead to misinformation, system compromise, data breaches, and denial of service.
## Exploitation
- Status: Varies by risk class; **Prompt Injection** and **Malicious Output** use is already prevalent.
- Complexity: Ranges from Low (some injection vectors) to Medium/High (complex model poisoning attacks).
- Attack Vector: Primarily **Network** (via user input or APIs) and logical (via manipulated data/training sets).
## Impact
- Confidentiality: High (e.g., Sensitive Information Disclosure, System Prompt Leakage)
- Integrity: High (e.g., Prompt Injection, Data/Model Poisoning, Hallucinations)
- Availability: Medium to High (e.g., Unbounded Consumption leading to DoS/cost overruns)
## Remediation
This section focuses on the mitigation strategies recommended by OWASP for each risk category identified.
### Patches
- **N/A**: No universal patch available; remediation requires implementation of security controls across the LLM lifecycle.
### Workarounds (Mitigation Strategies based on the 10 Risks)
| Risk Category | Mitigation Strategies |
| :--- | :--- |
| **1. Prompt Injection** | Data sanitization, input/output filtering, least privilege access controls, differential privacy, homomorphic encryption. |
| **2. Sensitive Info Disclosure** | Scrub training data, content filtering for sensitive output, robust access controls, response anonymization. |
| **3. Supply Chain** | Strict data governance, validating all third-party libraries/datasets, runtime monitoring. |
| **4. Data & Model Poisoning** | Strict vetting/securing of data sources during training, validation of model behavior post-training. |
| **5. Unsecure Output Handling** | Treat LLM output as untrusted input; apply strict input validation/sanitization on anything the output interacts with (e.g., databases, file systems). |
| **6. Excessive Agency** | Strict access controls defining tools/actions the LLM can execute; implement separation of duties. |
| **7. Overreliance** | Human oversight for high-stakes decisions, mandatory citation/fact-checking prompts, validation pipelines. |
| **8. Model Denial of Service** | Implement rate limiting on complex/long queries; enforce query complexity scoring before processing. |
| **9. Model Hallucination** | Use diverse/verified training data, require source citations, perform output validation/auditing. |
| **10. Unbounded Consumption** | Rate limiting, strict limits on input size/output length, timeouts on excessive operations, and input validation to reject resource-intensive requests. |
## Detection
- **Indicators of Compromise (IoCs):** Uncharacteristic model outputs, unusual spikes in API usage or resource consumption, unauthorized data appearing in responses, or unexpected changes in model behavior traceable to recent inputs.
- **Detection Methods and Tools:** Runtime monitoring for anomalous LLM behavior (e.g., deviation from baseline response patterns) and security tools focused on LLM guardrails (input/output validation layers).
## References
- OWASP Top 10 for LLM Applications 2025 Announcement: (Links defanged)
- OWASP LLM Project Sponsorship Program: (Links defanged)
- Guidance on Retrieval-Augmented Generation (RAG): (Links defanged)
- OWASP LLM AI Cybersecurity and Governance Checklist: (Links defanged)