OWASP GenAI Security Project 2025
Comprehensive Guide to LLM Security & AI Red Teaming

Generative AI is transforming business operations - but without proper security, it introduces unprecedented risks. The OWASP GenAI Security Project provides a comprehensive framework for securing Large Language Models (LLMs) and agentic AI systems against prompt injection, data leakage, and behavioral manipulation.

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What is OWASP GenAI? Why GenAI security matters Top 10 GenAI risks Agentic AI threats Security lifecycle ISECURION services FAQs

What Is the OWASP GenAI Security Project?

The OWASP GenAI Security Project is a community-driven initiative that addresses the unique security challenges of applications powered by Large Language Models and autonomous AI agents. Unlike traditional applications that follow deterministic code paths, GenAI systems interpret natural language, generate dynamic content, and make context-based decisions - creating entirely new attack surfaces.

Critical insight: Traditional security tools like web application firewalls and static code analysis only partially protect GenAI systems. Behavioral risks require specialized approaches.

Key OWASP GenAI Resources

OWASP Top 10 for LLM Applications

Prioritized list of the most critical security risks in LLM-powered applications, from prompt injection to insecure plugins.

OWASP Top 10 for Agentic AI

Specialized guidance for autonomous AI systems that can plan, reason, and take actions across multiple tools and APIs.

GenAI Red Teaming Guide

Practical methodology for adversarial testing of LLMs and AI agents to uncover vulnerabilities before attackers do.

CISO Checklist for GenAI

Executive-level governance framework for securing GenAI adoption across the enterprise.

Throughout this guide, we reference OWASP GenAI Security, LLM security assessment, GenAI VAPT, and AI red teaming to ensure clarity for security leaders evaluating AI security programs.

Why GenAI Security Is Fundamentally Different

The Behavioral Risk Paradigm

For decades, cybersecurity focused on securing predictable systems with defined logic. GenAI systems operate on probabilities and context, not deterministic rules. This creates behavioral risks that traditional security controls cannot fully address.

How GenAI systems differ from traditional applications

  • Natural language processing: Systems interpret human language, which attackers can manipulate through carefully crafted prompts.
  • Dynamic content generation: Outputs are created in real-time based on context, making it difficult to predict or validate responses.
  • Tool interaction: AI agents can execute API calls, query databases, and interact with external systems based on their reasoning.
  • Context-based decisions: Decisions are made based on training data, system prompts, and user input - all potential attack vectors.
Real-world impact: A chatbot can be tricked into revealing confidential data. An AI agent can be influenced to misuse legitimate tools. A model can unintentionally memorize and expose sensitive training data.

OWASP Top 10 GenAI Security Risks

The OWASP GenAI Security Project identifies the most critical risks affecting Large Language Models (LLMs) and AI-driven applications. These risks are actively exploited in production environments and must be addressed before deploying GenAI systems at scale.

LLM-01
Prompt Injection

Malicious prompts override system instructions, bypass safety controls, manipulate outputs, or extract confidential data.

Impact: Data leakage, control bypass, unauthorized actions

LLM-02
Sensitive Data Exposure

AI systems handling proprietary or regulated data may unintentionally expose sensitive information through responses, logs, or training reuse.

Impact: Privacy violations, regulatory penalties, reputational damage

LLM-03
Model Supply Chain Risks

Compromised datasets, poisoned pre-trained models, or malicious dependencies can silently alter model behavior.

Impact: Backdoors, bias injection, long-term compromise

LLM-04
Insecure Integrations

Weak authentication or excessive permissions in connected APIs and systems can turn AI into a powerful attack vector.

Impact: Lateral movement, privilege escalation, data exfiltration

LLM-05
Training Data Poisoning

Malicious or biased data injected during training or fine-tuning embeds hidden behaviors into AI models.

Impact: Model integrity loss, persistent vulnerabilities

LLM-06
Model Denial of Service (DoS)

Resource-intensive prompts or abusive usage can exhaust compute capacity, degrading availability or causing outages.

Impact: Service disruption, financial loss, SLA violations

LLM-07
Insecure Plugin & Extension Design

Third-party plugins may introduce vulnerable dependencies, excessive permissions, or unsafe execution paths.

Impact: Remote code execution, system compromise

LLM-08
Excessive Agency

Overly autonomous AI systems may execute actions without human oversight or approval.

Impact: Unauthorized actions, compliance failures

LLM-09
Overreliance on AI Outputs

Blind trust in AI-generated responses can lead to decisions based on hallucinated or manipulated information.

Impact: Poor decisions, legal exposure, financial loss

LLM-10
Model Theft

Proprietary models can be extracted through inference abuse, repeated querying, or unauthorized access.

Impact: IP theft, loss of competitive advantage

Agentic AI: Why the Risk Multiplies

Traditional chatbots respond to queries. Agentic AI systems actively plan and execute tasks - they decide what to do next, choose tools, chain actions together, and operate with minimal human oversight. This autonomy dramatically increases both capability and risk.

Key distinction: A compromised agent can execute unauthorized API calls, modify data, or exfiltrate information using legitimate tools - all while appearing "authorized" to security systems.

OWASP Top Risks for Agentic AI Systems

Agent Behavior Hijacking

Attackers manipulate agent reasoning to execute malicious multi-step workflows that achieve attacker objectives.

Identity & Privilege Abuse

Service identities with excessive permissions enable agents to escalate privileges or impersonate trusted systems.

Tool Misuse

Agents with access to powerful tools (APIs, databases, email) can be tricked into using them for unauthorized purposes.

OWASP emphasizes that agentic systems require stronger governance, runtime policy enforcement, deep visibility into agent decisions, and continuous red teaming to ensure safe operation.

OWASP's Security Lifecycle for GenAI (LLMSecOps)

OWASP advocates a lifecycle-based approach called LLMSecOps - embedding security across every phase of GenAI development and operation.

1
Governance & Inventory

Identify all GenAI usage, document models and integrations, define acceptable use policies.

2
Secure Design

Threat model GenAI systems considering prompt manipulation, data leakage, and agent misuse scenarios.

3
Secure Development

Protect training pipelines, fine-tuning workflows, and model artifact management.

4
Runtime Controls

Enforce input validation, output filtering, policy enforcement, and least-privilege tool access.

5
Testing & Red Teaming

Challenge security assumptions through prompt injection testing, jailbreak testing, and agentic attack simulations.

6
Monitoring & Response

Log prompts, responses, and agent actions. Detect abnormal behavior and maintain incident response playbooks.

Critical controls at each lifecycle stage

Design Phase

Use OWASP Top 10 as threat modeling foundation. Map data flows, identify trust boundaries, and document sensitive information handling.

Development Phase

Implement secure prompt templates, validate training data sources, use trusted model repositories, and apply least-privilege principles.

Runtime Phase

Deploy LLM firewalls, filter inputs/outputs, enforce rate limits, implement guardrails, and maintain audit logs.

Operations Phase

Monitor for anomalies, track model drift, maintain incident response procedures, and conduct regular security assessments.

How ISECURION Secures GenAI Systems End-to-End

ISECURION brings together offensive security expertise, secure engineering, and compliance leadership to help organizations adopt GenAI safely. We integrate AI security into existing cybersecurity programs rather than treating it as a separate problem.

GenAI Asset Discovery & Risk Profiling

We identify all GenAI usage across your organization, including shadow AI and undocumented integrations. This discovery forms the foundation of your GenAI security program.

Deliverables: Comprehensive AI inventory, risk scoring, shadow AI report

OWASP-Aligned Threat Modeling

Structured GenAI risk assessments aligned with OWASP guidance. We map your specific use cases to the Top 10 risks and identify priority remediation areas.

Deliverables: Threat models, OWASP risk mapping, prioritized roadmap

GenAI VAPT (LLM & Agentic Testing)

Our GenAI vulnerability assessment and penetration testing goes beyond traditional testing with prompt injection testing, jailbreak attempts, output manipulation validation, API security testing, and agent workflow abuse simulations.

Deliverables: Technical findings, proof-of-concepts, remediation guidance

GenAI Red Teaming Services

Realistic adversary simulation targeting your GenAI systems. We test multi-step agent attacks, tool misuse scenarios, identity abuse, and model behavior manipulation.

Deliverables: Attack scenarios, business impact analysis, detection recommendations

Secure DevSecOps for AI Pipelines

We integrate security into model training and fine-tuning pipelines, CI/CD workflows, and infrastructure-as-code for AI deployments to prevent insecure models from reaching production.

Deliverables: Secure pipeline architecture, automation scripts, policy templates

Runtime Monitoring & Incident Response

Implementation of runtime policy enforcement, AI-aware logging and telemetry, detection rules for abnormal AI behavior, and incident response playbooks specific to GenAI incidents.

Deliverables: Monitoring dashboard, detection rules, IR playbooks

Compliance & Audit Readiness

We map GenAI controls to ISO 27001, SOC 2, DPDP Act, and ISO 42001 (AI Management Systems) to ensure GenAI adoption doesn't introduce compliance blind spots.

Deliverables: Control mapping, gap analysis, compliance roadmap

GenAI Security Training

Hands-on training for development teams, security teams, and executives on GenAI security best practices, prompt injection defense, and secure AI development.

Deliverables: Training materials, workshops, knowledge transfer sessions

Business Value of GenAI Security

Securing GenAI isn't about slowing innovation - it enables it. Organizations that invest early in GenAI security gain competitive advantages.

Protect Sensitive Data

Prevent data leakage and unauthorized access to proprietary information through AI systems.

Avoid Costly Incidents

Prevent security breaches, regulatory penalties, and reputational damage from AI vulnerabilities.

Build Customer Trust

Demonstrate responsible AI deployment that protects customer data and privacy.

Enable Safe Innovation

Deploy AI capabilities faster with confidence in security controls and risk management.

Industry-Specific GenAI Security Use Cases

Banking & Financial Services

GenAI security for banking focuses on protecting transaction data, preventing fraud through AI manipulation, and securing AI-powered financial advisory systems. Critical concerns include prompt injection attacks on chatbots handling account information and ensuring AI decisions comply with financial regulations.

Healthcare

Healthcare GenAI security addresses PHI protection, securing AI diagnostic tools, and preventing manipulation of treatment recommendations. HIPAA compliance requires special attention to how patient data flows through AI systems and ensuring outputs don't inadvertently expose protected health information.

Fintech & Payments

Fintech GenAI security emphasizes API security for AI-powered services, preventing business logic abuse in AI recommendation engines, and securing autonomous trading or lending decision systems. Agent-based financial systems require robust guardrails to prevent unauthorized transactions.

Government & Public Sector

Government GenAI deployments require heightened security due to sensitive data handling and public accountability. Security focuses on preventing AI manipulation that could affect policy decisions, protecting citizen data, and ensuring transparent, auditable AI decision-making processes.

E-Commerce & Retail

Retail GenAI security addresses protecting customer data in recommendation systems, preventing manipulation of pricing or inventory AI, and securing conversational commerce chatbots. Special attention to preventing attackers from using AI to gain unauthorized discounts or access to customer information.

Professional Services

Professional services firms deploying GenAI for document analysis, client communications, and knowledge management must ensure client confidentiality, prevent cross-contamination of client data, and maintain attorney-client or similar professional privileges in AI interactions.

GenAI Security Assessment Timeline & Process

A typical ISECURION GenAI security assessment follows a structured approach that delivers actionable results within 2-4 weeks, depending on scope complexity.

Week 1: Discovery & Scoping
  • Identify GenAI systems and integrations
  • Document data flows and trust boundaries
  • Map to OWASP Top 10 risk categories
  • Define testing scope and rules of engagement
Week 2-3: Active Testing
  • Prompt injection and jailbreak testing
  • API and integration security testing
  • Agent workflow abuse simulations
  • Data leakage validation
  • Model behavior analysis
Week 4: Reporting & Remediation
  • Detailed findings with proof-of-concepts
  • Prioritized remediation roadmap
  • Executive summary for leadership
  • Technical debriefing with security team
Ongoing: Continuous Validation
  • Retesting after remediation
  • Quarterly security assessments
  • Continuous red teaming programs
  • Security posture monitoring

Frequently Asked Questions - OWASP GenAI Security

The OWASP GenAI Security Project is an open, community-led initiative focused on securing applications that use Large Language Models (LLMs) and autonomous AI agents. It provides resources like the Top 10 risks, red teaming guides, and security checklists specifically for GenAI systems.

GenAI systems behave differently from traditional applications - they generate probabilistic outputs, interact with multiple tools, and can act autonomously. These characteristics create unique risks like prompt injection, sensitive data leakage, agentic abuse, and model supply chain attacks that traditional security controls cannot fully address.

Prompt injection occurs when an attacker manipulates the input to a GenAI system to override its instructions, exfiltrate data, or produce malicious outputs. Without proper defenses, even trusted AI systems can be exploited through carefully crafted prompts that the model cannot distinguish from legitimate instructions.

ISECURION provides end-to-end GenAI security services including asset discovery and risk profiling, OWASP-aligned threat modeling, LLM and agentic VAPT, GenAI red teaming, secure AI DevSecOps integration, runtime monitoring and incident response, and compliance mapping for SOC 2, ISO 27001, and DPDP Act.

Agentic AI systems can plan and execute tasks autonomously. Unlike simple chatbots, they can call APIs, modify data, or chain actions together. This creates multi-step attack paths where a compromised agent can execute unauthorized operations while appearing legitimate, significantly increasing both impact and complexity of potential security incidents.

OWASP recommends a lifecycle-based approach (LLMSecOps) including governance and inventory, secure design and threat modeling, secure development and data controls, runtime security controls, testing and red teaming, and monitoring and incident response.

Depending on the size of the deployment, risk profile, and number of agents or models, ISECURION's GenAI VAPT and red teaming assessments typically take 2-4 weeks. This includes discovery, testing, remediation guidance, and reporting.

Yes. By implementing OWASP-aligned security controls, organizations can map GenAI systems to SOC 2, ISO 27001, ISO 42001, and DPDP Act requirements, ensuring secure AI adoption without introducing compliance gaps.

Securing GenAI protects sensitive data and intellectual property, reduces risk of regulatory penalties and brand damage, builds trust with customers and partners, and enables faster, safer AI innovation. Organizations that invest early gain competitive advantages through responsible AI deployment.

GenAI red teaming simulates real adversaries targeting your AI systems. It includes testing multi-step agent attacks, tool misuse scenarios, identity and privilege abuse, model behavior manipulation, and data exfiltration paths. The goal is to validate detection capabilities and identify business-impacting vulnerabilities before real attackers do.

GenAI VAPT focuses on AI-specific attack vectors like prompt injection, model behavior manipulation, training data poisoning, and agentic workflow abuse. Traditional penetration testing covers infrastructure and application vulnerabilities but doesn't address the unique behavioral risks of AI systems that operate on probabilities rather than deterministic code.

Banking, fintech, healthcare, government, and any industry handling sensitive data or operating under strict compliance requirements benefit significantly. However, any organization deploying customer-facing AI, internal AI copilots, or autonomous AI agents should conduct GenAI security assessments to manage risk effectively.

Shadow AI refers to AI tools and systems deployed without IT or security oversight. Employees may use public AI services, integrate unsanctioned AI plugins, or deploy unauthorized AI agents, creating data leakage risks, compliance violations, and security blind spots that attackers can exploit.

Initial assessments should be conducted before production deployment. Follow-up assessments are recommended quarterly or after major changes to AI systems, training data, or integrations. High-risk deployments benefit from continuous red teaming programs that provide ongoing validation.

Deliverables include an executive summary, detailed technical findings with proof-of-concepts, OWASP Top 10 risk mapping, prioritized remediation roadmap, detection recommendations for security teams, and compliance gap analysis. Some assessments also include remediation workshops to ensure findings are properly addressed.

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