OWASP Top 10

OWASP Top 10 (AI) refers to the Open Worldwide Application Security Project’s (OWASP) authoritative list highlighting the most critical security risks specifically associated with Artificial Intelligence (AI) and Large Language Model (LLM) applications. Released as a specialized extension of OWASP’s long-established Top 10 security risks frameworks, this list identifies the prevalent vulnerabilities and threats in AI systems, guiding organizations to prioritize defenses and governance around AI security.

The OWASP Top 10 for AI and LLM applications catalogs the most impactful and common attack vectors found across AI deployments, focusing on how adversaries exploit the unique capabilities and risks of generative AI models. The 2023 and 2024 versions of the OWASP AI Top 10 emphasize threats that span input manipulation, data integrity, model misuse, and governance weaknesses.

Core AI Security Risks in OWASP Top 10

1. Prompt Injection: Attackers craft malicious inputs that manipulate large language models into executing unintended commands, disclosing sensitive data or bypassing security controls. This risk is particularly dangerous because it exploits the very interface where users provide natural language prompts OWASP LLM Top 10.
2. Insecure Output Handling: Failure to validate, sanitize, or filter outputs generated by AI models can lead to downstream vulnerabilities such as cross-site scripting (XSS), remote code execution (RCE), or privilege escalation attacks, especially when AI outputs are integrated into broader application workflows Cloudflare OWASP LLM.
3. Training Data Poisoning: Manipulation or contamination of training datasets can degrade model accuracy, introduce biased or malicious behavior, and weaken overall AI system trustworthiness. Poisoned data can come from malicious actors or poor data governance Trend Micro AI Risks.
4. Model Denial of Service (DoS): Attackers may overwhelm AI services with excessive or computationally expensive requests, causing service interruptions or excessive operational costs through resource exhaustion.
5. Supply Chain Vulnerabilities: Dependencies on third-party AI components, datasets, or plugins may introduce hidden malware or backdoors, exposing organizations to exploitation through compromised AI ecosystem partners.
6. Sensitive Information Disclosure: Improper safeguarding of personal or proprietary data during AI model training or runtime can lead to unintentional exposure of confidential information, violating privacy regulations and business confidentiality.
7. Insecure Plugin Design: AI plugins that ingest untrusted inputs or operate with excessive permissions can be exploited for remote code execution or unauthorized data access.
8. Excessive Agency: Granting AI systems uncontrolled autonomy to take actions without human oversight risks unintended, damaging behaviors, threatening privacy, and undermining user trust Bugcrowd on Excessive Agency.
9. Overreliance on AI: Blind trust in AI outputs without human verification can propagate misinformation, bias, or legal liabilities.
10. Model Theft: Unauthorized access or exfiltration of proprietary AI models may lead to competitive disadvantage and further attack risks OWASP AI Gen.

Categories and Impact

OWASP groups these risks into three broad categories:

• Access Risks: Unauthorized privilege escalations or misuse of AI capabilities like insecure plugin designs and excessive agency.
• Data Risks: Issues such as data poisoning, supply chain compromises, and leakage of sensitive information.
• Reputational and Business Risks: Including model theft and organizational overreliance on AI outputs.

The evolving OWASP Top 10 offers a dynamic framework that AI developers, security teams, and governance bodies use to proactively identify, mitigate, and monitor AI vulnerabilities before they can be exploited Trend Micro.

How PointGuard AI Tackles Related Security Challenges

PointGuard AI integrates advanced AI security controls that align with OWASP Top 10 principles to protect AI environments comprehensively. Its platform performs continuous discovery of AI models, autonomous agents, plugins, datasets, and data flows across hybrid cloud and enterprise systems.

PointGuard AI detects prompt injection attempts, monitors output integrity, and applies runtime filters to prevent malicious inputs and unsafe AI responses. It identifies supply chain risks through metadata and provenance analysis and mitigates excessive agency by enforcing policy-based controls on AI actions.

The platform also protects sensitive information by implementing fine-grained data loss prevention (DLP) tailored for AI interactions and rigorously enforces access controls on AI components. Automated threat correlation and anomaly detection help reduce false positives and enable focused response on genuine threats drawn from OWASP Top 10 scenarios.

By fusing discovery, contextual risk analysis, and runtime defense closely following recognized industry standards, PointGuard AI empowers organizations to secure AI innovation reliably, meet compliance mandates, and maintain trust in AI-driven services.

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References:

OWASP Top 10

CSO Online: What is OWASP and why it matters