Explore how OWASP's Top 10, the EU AI Act, and Promptention's adaptive security platform shape the future of LLM safety and compliance.
Risks and Regulations Collide
The explosive adoption of large language models (LLMs) and generative AI tools has outpaced the security protocols and legal frameworks that typically govern new technologies. In the rush to deploy LLMs into production environments, many organizations have found themselves unprepared for the new wave of risks these models.
In 2025, we are no longer asking whether LLMs should be regulated, we are deep in the process. With the OWASP Top 10 for LLM Applications and the enforcement of the EU AI Act, the pressure on companies to align their AI deployments with best practices and regulatory expectations has never been greater.
This blog breaks down:
- What OWASP's Top 10 list reveals about the evolving threat landscape
- What the EU AI Act mandates for LLM developers and users
- Why guardrails are not optional but essential
- How Promptention helps enterprises stay ahead of both threats and compliance standards
What Is OWASP and Why Their Top 10 List Matters for AI
The Open Worldwide Application Security Project (OWASP) is a globally recognized authority on cybersecurity best practices. While originally focused on traditional web application threats (e.g., SQL injection, XSS), OWASP has now turned its attention to the unique risks posed by large language models.
Their "Top 10 for LLM Applications", first published in 2023 and updated in a 2025 edition released in November 2024, is designed to raise awareness of the most pressing security vulnerabilities in AI systems. It serves as both a cautionary checklist and a practical guide for organizations deploying LLMs.
Here's a summary of OWASP's Top 10 LLM risks:
- LLM01 Prompt Injection - Inputs, direct or hidden in content the model reads, alter its behaviour in unintended ways.
- LLM02 Sensitive Information Disclosure - The model reveals personal data, credentials, or confidential business information.
- LLM03 Supply Chain - Third-party models, datasets, plugins, and dependencies introduce risk.
- LLM04 Data and Model Poisoning - Manipulated training, fine-tuning, or embedding data plants backdoors or bias.
- LLM05 Improper Output Handling - Model output reaches downstream systems without validation.
- LLM06 Excessive Agency - The model has more functionality, permissions, or autonomy than its task needs.
- LLM07 System Prompt Leakage - Instructions or secrets placed in the system prompt are exposed.
- LLM08 Vector and Embedding Weaknesses - Retrieval pipelines and vector stores are poisoned or leak across users.
- LLM09 Misinformation - Confident but false output is trusted and acted on.
- LLM10 Unbounded Consumption - Uncontrolled use drives denial of service, runaway cost, or model extraction.
These risks are not just theoretical. LLMs must be treated as dynamic, adversarial systems, not static tools.
The EU AI Act: What It Requires in 2025
The EU AI Act, passed in 2024 and entering phased enforcement in 2025, is the world's most comprehensive regulation of artificial intelligence to date. It introduces a risk-based framework to classify AI systems and impose corresponding requirements.
Key elements relevant to LLMs and GenAI applications:
- High-Risk Classification: AI systems used for purposes the Act lists, such as recruitment, creditworthiness assessment, access to essential services, or the administration of justice, are high-risk and need documentation, risk management, human oversight, and post-market monitoring. An LLM falls into this category when it is used for one of those purposes.
- Transparency Obligations: Users must be notified when they're interacting with an AI system, especially in decision-making contexts.
- Robustness and Accuracy: Providers must implement safeguards against manipulation (e.g., prompt attacks), error propagation, and misuse.
- Traceability and Logging: High-risk systems must automatically record events so their operation can be traced and audited.
- Data Governance: For high-risk systems, training data must be relevant, representative, and examined for possible biases.
The AI Act has also triggered a broader international regulatory movement. It aligns closely with global principles from OECD and intersects with GDPR in areas like data protection, automated profiling, and explainability.
Violations can carry fines of up to €35 million or 7% of global turnover, making compliance not just ethical but financially critical.
The Risks in Context: Why Guardrails Are Needed
The technical and legal risks now go hand in hand. Consider these scenarios, each a pattern that adversarial testing looks for:
- Hidden instructions planted in user profiles lead a customer service LLM to reveal internal company data.
- A healthcare chatbot with access to patient records is prompted to disclose symptoms and private information.
- A RAG-based assistant used in legal discovery is tricked into misrepresenting evidence through poisoned retrieval content.
These are the patterns adversarial testing is designed to surface. And as LLMs become more powerful, their ability to trigger business logic, touch real data, or interface with critical infrastructure means the cost of failure will only rise.
Traditional defenses, blocklists, regex filters, and input validation, are insufficient in a world where attacks evolve at the speed of language. Companies require threat detection systems and risk management systems.
Promptention's Approach: Secure by Design, Adaptive by Default
At Promptention, we've built our platform to meet the evolving needs of LLM security in both enterprise and regulatory environments.
We provide:
- Prompt monitoring and logs across all applications
- PII detection
- Strict prompt injection detection, adaptable to new attack types
- Customizable security policies for different business units
- Real-time alerts with contextual scores, reducing noise and false positives
Our detection layer evolves daily, powered by a rich and expanding dataset of adversarial examples. We do not rely on static filters. Our platform is built not just to respond but to anticipate any kind of prompt attacks.
Whether you're preparing for an EU AI Act audit, mitigating OWASP-class threats, or simply trying to protect your company from reputational risk, Promptention offers a solution that's proactive, precise, and ready for scale.
Conclusion
The convergence of regulation (EU AI Act, GDPR), frameworks (OWASP), and rising enterprise deployment has changed the equation. Security for LLMs is no longer optional. It's foundational.
Guardrails must be:
- Flexible enough to evolve with adversarial techniques
- Precise enough to minimize business disruption
- Context-aware enough to recognize real threats from normal queries
Promptention is here to protect it.
