Identify Al
with TrustAl Evaluation
Real-time identification, automatically configured to address attacks of each Al chatbot.
Are your models secure and safe?
Al Validation uses over 150 security and safety categories when performing algorithmic testing of models, which can find vulnerabilities to malicious actions, such as prompt injection and data poisoning, or unintentional outcomes. There are four primary failure categories:
Abuse Failures
Toxicity, bias, hate speech, violence, sexual content, malicious use, malicious code generation, disinformation
Privacy Failures
PIl leakage, data loss, model information leakage, privacy infringement
Integrity Failures
Factual inconsistency, hallucination, off-topic, off-policy
Availability Failures
Denial of service, increased computational cost
Generate evaluation report for your AI
Unlock AI-powered security with seamless scanning: Simply input your target URL for a comprehensive, automated assessment, compliant with OWASP, NIST, and MITRE ATLAS standards, delivering detailed reports in no time.
Foundation Models Evaluation
Foundation models are at the core of most Al applications today, either modified with fine-tuning or purposebuilt. Learn what challenges need to be addressed to keep models safe and secure.
Red teaming for Base model
Retrieval-augmented generation is quickly becoming a standard to add rich context to LLM applications.Learn about the specific security and safety implications of RAG.
Red teaming for Al Chatbots & Agents
Chatbots are a popular LLM application, and autonomous agents that take actions on behalf of users are starting to emerge. Learn about their security and safety risks.
Key Features
Preserving Your Data, Securing Your Future, and Ensuring Lasting AI Integrity.
Extensive Attack Library
DeepEval’s extensive Attack Library spans six major threat categories and is continuously updated to counter zero-day and emerging attack techniques. Using trained LLMs as detectors, DeepEval delivers accuracy, not just coverage, to ensure your systems remain protected against a wide range of evolving vulnerabilities with limited false positives.
Augmented Red teaming
Red teamers can work collaboratively with DeepEval, using Natural Language to set attack goals—no code necessary. DeepEval produces in-depth, conversation-level visibility to support risk analysis and remediation.
Reporting and Framework Mapping
DeepEval easily exports results to CSV and JSON to enable quick and effective collaboration across teams. Vulnerabilities are also mapped to standard frameworks such as OWASP Top 10 for LLMs, NIST, MITRE ATLAS, and more, making it fast and easy to meet compliance standards.
Secure your AI today!
– Teams across your organization are building GenAI products that create exposure to AI-specific risks.
– Your existing security solutions don’t address the new AI threat landscape.
– You don’t have a system to identify and flag LLM attacks to your SOC team.
– You have to secure your LLM applications without compromising latency.
– Your product teams are building AI applications or using 3rd party AI applications without much oversight.
– Your LLM apps are exposed to untrusted data and you need a solution to prevent that data from harming the system.
– You need to demonstrate to customers that their LLM applications are safe and secure.
– You want to build GenAI applications but the deployment is blocked or slowed down because of security concerns.