Darkhunt AI Security vs DeepTeam

Darkhunt AI Security continuously tests, monitors, and protects AI agents in production. It discovers models, agents, and MCP servers across SaaS, cloud, endpoints, and code, including sanctioned and shadow AI. Its observability tools log and replay activity, making agent prompts, responses, and tool calls searchable. Automated red teaming tests agents with their actual tools, permissions, and data access for prompt injection, jailbreaks, and data exfiltration. The platform also checks decision integrity, including manipulated figures, suppressed alerts, RAG poisoning, and confident hallucinations, and traces answers to source data points. Findings can become inline runtime policies, while an MCP gateway proxies requests and responses. Listed integrations include OpenAI, Anthropic, Azure, AWS Bedrock, Gemini, and self-hosted systems. Deployment options are cloud, hybrid, and self-hosted. Findings or controls can map to several named frameworks, including the EU AI Act and NIST AI RMF. The free plan costs nothing and includes full platform access, a free credit allotment, and community support. Paid options have unlisted prices.

DeepTeam is an open-source Python framework for red teaming large language model systems. It offers more than 50 ready-to-use vulnerability checks covering bias, toxicity, personal-information leakage, prompt leakage, and security risks. Simulated attacks include jailbreaks, prompt injection, automated evasion, data extraction, and response manipulation. Vulnerability-specific LLM-as-a-Judge metrics return a binary score with reasoning. Supported reference frameworks include OWASP, NIST AI RMF, MITRE ATLAS, the EU AI Act, BeaverTails, and Aegis. Listed model providers include OpenAI, Claude, Gemini, Azure OpenAI, AWS Bedrock, Vertex AI, Mistral, LiteLLM, and Portkey. CI/CD listings include GitHub Actions, GitLab CI, Jenkins, CircleCI, Buildkite, and Azure Pipelines. Installation uses pip in a Python virtual environment, and attacks are dynamically simulated without a prepared dataset. Results can be saved locally or sent to Confident AI for centralized viewing and production red teaming through a UI. The framework is Apache 2.0 licensed and supports self-hosted or API use.