Protect your AI applications and agents from attacks, fakes, unauthorized access, and malicious data inputs.
Control your GenAI applications and agents and assure their alignment with their business purpose.
Proactively test GenAI models, agents, and applications before attackers or users do
The only real-time multi-language multimodality technology to ensure your brand safety and alignment with your GenAI applications.
Ensure your app is compliant with changing regulations around the world across industries.
Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
Detect and prevent malicious prompts, misuse, and data leaks to ensure your conversational AI remains safe, compliant, and trustworthy.
Protect critical AI-powered applications from adversarial attacks, unauthorized access, and model exploitation across environments.
Provide enterprise-wide AI security and governance, enabling teams to innovate safely while meeting internal risk standards.
Safeguard user-facing AI products by blocking harmful content, preserving brand reputation, and maintaining policy compliance.
Secure autonomous agents against malicious instructions, data exfiltration, and regulatory violations across industries.
Ensure hosted AI services are protected from emerging threats, maintaining secure, reliable, and trusted deployments.
If you are concerned about how well your AI Safety and Security defenses address critical web application threats, you’re not alone.
A recent TechRadar analysis found that 64% of businesses worry about the integrity of AI systems, and 57% cite “trustworthiness” as a top challenge. These numbers show that concern extends beyond basic security to the reliability and governance of AI in production environments. Similarly, a VentureBeat report on PwC’s latest CEO survey revealed that 77% of global CEOs are concerned about AI cybersecurity risks. Both findings underscore that anxiety about AI security is not isolated to security teams. It’s a board-level priority with direct implications for strategic risk management.
Any effective AI Safety and Security program begins with strong guardrails defined in sets of policies. These policies define the practical rules, controls, and safeguards that help generative or decision-making AI systems remain safe, compliant, and aligned with the organization’s goals.
Because every organization has unique objectives and risks, these policies must be customized. This leads to an important question: how do you determine which threats your policies should address?
The Open Web Application Security Project (OWASP) can help. OWASP is a nonprofit organization that provides free, open-source resources and best practices to help secure web applications and AI systems.
Each year, OWASP publishes its Top 10 LLM (Large Language Model) Security Threats, identifying the most critical risks facing AI applications today. For product leaders and executives, aligning policies and safeguards with this list is essential. It helps protect users, secure data, and reduce the risk of AI misuse or unintended behavior that could harm the organization’s reputation.
Let’s dive into each of 2025’s OWASP Top Ten risks and see how ActiveFence Guardrails keeps your AI Applications secure against each by mapping them to our out-of-the box AI safety and security policies.
Prompt injection happens when user input manipulates an LLM’s instructions or output, potentially overriding safeguards. These attacks can take direct or indirect forms and may lead to the model revealing confidential data, ignoring safety rules, or executing unauthorized actions.
Applicable ActiveFence Policies:
Large language models may unintentionally reveal sensitive information, such as personally identifiable information(PII), credentials, or internal system details. This often results from training data leakage or when user prompts and system context are reused or improperly sanitized.
LLM systems frequently rely on third-party tools, models, and datasets, which can introduce risk if compromised or untrusted. Attackers may exploit outdated dependencies, tamper with plugins, or insert malicious content during model development or deployment.
Applicable ActiveFence Guardrails Policies:
When training or fine-tuning data is manipulated, models can be biased, destabilized, or compromised with hidden triggers. Poisoned models may appear normal but can be activated by specific prompts to behave maliciously or produce harmful outputs.
If LLM-generated content is trusted and used by downstream systems without validation, it can result in vulnerabilities like code injection, cross-site scripting, or phishing. Outputs should always be treated as untrusted and validated before use.
Granting LLMs broad permissions or tool access can lead to unintended actions, especially in agent-like configurations. Without strict limitations and oversight, these models might modify files, access internal systems, or perform actions beyond their intended scope.
System prompts contain critical instructions or context that guide model behavior. If attackers extract this information, they can craft more effective jailbreaks, bypass filters, or gain insight into internal logic and data structures.
Retrieval-augmented generation (RAG) systems and embedding models can be exploited through poisoned inputs or embedding inversion. Weak validation or access controls in vector databases may expose sensitive information or lead to manipulated model responses.
LLMs are prone to generating false but convincing outputs, often due to hallucinations, outdated training data, or poorly scoped tasks. When these inaccuracies are accepted as truth, they can mislead users, damage trust, and create reputational or legal risk.
Without proper limits, attackers can abuse LLMs by sending large volumes of requests that consume compute resources, escalate costs, or extract model details. This includes denial-of-wallet attacks and model extraction techniques that can compromise system integrity.
ActiveFence Guardrails comes equipped with powerful out-of-the-box policies designed to defend against the most pressing risks in the OWASP Top Ten, giving organizations a strong baseline for AI safety and security from day one. But one-size-fits-all protection is not enough. Since every organization has its own values, user expectations, and risk profiles, ActiveFence goes further, allowing you to define and enforce custom policies tailored to your specific use cases, brand standards, and regulatory requirements. This combination of proven defaults and flexible customization ensures your AI systems stay safe, aligned, and trustworthy in the real world.
Real-time guardrails are an essential layer of defense, but some threats fall outside their scope. In addition, policies must continuously adapt to address new and emerging risks. Continuous red teaming helps by actively probing AI systems for weaknesses, using adversarial techniques to uncover gaps that static rules or filters might miss. The insights from these tests are then used to update and strengthen guardrails, ensuring they stay effective against the latest attack methods. This feedback loop between ActiveFence Red Teaming and ActiveFence Guardrails allows security and product teams to stay ahead of threats and maintain trust in their AI systems.
Securing AI applications is a strategic priority for any organization using AI in production. ActiveFence offers a powerful combination of ready-to-use protections, customizable policies, and continuous red teaming to help you stay ahead of evolving threats.
Request a demo today, and see how you can strengthen your AI safety and security program with ActiveFence.
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