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| title | sidebar_position | description | tags | keywords |
|---|---|---|---|---|
| Release Notes | 100 | Track monthly Promptfoo releases featuring new providers, security plugins, performance improvements, and community contributions | [releases changelog updates features] | [Promptfoo releases changelog updates features monthly summaries] |
Full release history for Promptfoo open source can be found on GitHub.
This month we focused on expanding provider support, enhancing evaluation capabilities, and strengthening enterprise features to help you build more reliable and secure AI applications.
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import pdb; pdb.set_trace() in executed third-party Python scripts for easier debuggingWe've significantly improved the evaluation results interface to handle large-scale testing more effectively:
First-Class Zooming Support - Zoom in and out of the eval results table to see more data at once or focus on specific details. This is especially useful when working with evaluations containing hundreds or thousands of test cases.
Advanced Metadata Filtering - Filter results using powerful search operators (equals, contains, not contains) with persistent button actions. Click on any metric pill in the results to instantly apply it as a filter, making it easier to drill down into specific failure modes or success patterns.
Improved Pagination - Enhanced pagination controls with "go to" functionality and better handling of large result sets. The UI now maintains scroll position and filter state as you navigate between pages.
Multi-Metric Filtering - Apply multiple filters simultaneously to find exactly the results you're looking for. For red team evaluations, you can now filter by both plugin and strategy to analyze specific attack vectors.
Performance Optimizations - Fixed horizontal scrolling issues, improved rendering performance for large tables, and optimized memory usage when dealing with extensive evaluation results.
These improvements make it much easier to analyze and understand evaluation results, especially for large-scale red teaming exercises or comprehensive test suites.
We've launched two powerful new agentic multi-turn red team strategies that adapt dynamically based on target responses:
Custom Strategy - Define your own red teaming strategies using natural language instructions. This groundbreaking feature lets you create sophisticated, domain-specific attack patterns without writing code. The AI agent interprets your instructions and executes multi-turn conversations tailored to your specific testing needs.
Mischievous User Strategy - Simulates an innocently mischievous user who plays subtle games with your AI agent through multi-turn conversations. This strategy uncovers vulnerabilities by mimicking real-world user behavior where users might push boundaries through playful or indirect approaches rather than direct attacks.
Both strategies leverage AI agents to conduct intelligent, adaptive conversations that evolve based on your system's responses, making them far more effective than static attack patterns.
This month we focused on enhancing observability, expanding provider support, and strengthening red team capabilities to help you build more reliable and secure AI applications.
We've added OpenTelemetry tracing support to help you understand what's happening inside your AI applications. Previously, LLM applications were often "black boxes"—you could see inputs and outputs, but not what happened in between. Now you can visualize the entire execution flow, measure performance of individual steps, and quickly identify issues.
This is especially valuable for complex RAG pipelines or multi-step workflows where you need to identify performance bottlenecks or debug failures.
Use it when:
As AI applications increasingly use voice interfaces and visual content, you need tools to evaluate these capabilities just as rigorously as text-based interactions. We've significantly expanded support for audio and multimodal AI:
Google Live Audio - Full audio generation with features like:
Hyperbolic Provider - New support for Hyperbolic's image and audio models, providing more options for multimodal evaluations
Helicone AI Gateway - Route requests through Helicone for enhanced monitoring and analytics
Mistral Magistral - Added support for Mistral's latest reasoning models
Supply chain attacks through compromised models are a growing threat. We've significantly enhanced our static model security scanner to help you verify model integrity before deployment, checking for everything from malicious pickle files to subtle statistical anomalies that might indicate trojaned models.
New Web Interface: ModelAudit now includes a visual UI accessible at /model-audit when running promptfoo view:
Expanded Format Support:
.bin files (PyTorch, SafeTensors, etc.)Security Improvements:
PROMPTFOO_MAX_EVAL_TIME_MS environment variable prevents runaway evaluations from consuming excessive resourcesGeneric attacks often miss system-specific vulnerabilities. We've added powerful features for organizations that need sophisticated AI security testing to create targeted tests that match your actual security risks:
Target Discovery Agent - Automatically analyzes your AI system to understand its capabilities and craft more effective, targeted attacks
Adaptive Red Team Strategies - Define complex multi-turn attack strategies with enhanced capabilities for targeted testing
Grader Customization - Fine-tune evaluation criteria at the plugin level with concrete examples for more accurate assessments
Cloud-based Plugin Severity Overrides - Enterprise users can centrally manage and customize severity levels for red team plugins across their organization
Different industries face unique AI risks. We've introduced specialized plugins for industries where AI errors have serious consequences, ensuring you're testing for the failures that matter most in your domain:
Medical Plugins detect critical healthcare risks:
Financial Plugins identify domain-specific vulnerabilities:
Biased AI systems can perpetuate discrimination at scale. Our new comprehensive bias detection tests ensure your AI treats all users fairly and respectfully across:
The Intent (Custom Prompts) plugin now supports JSON file uploads with nested arrays for multi-step attack sequences. The enhanced UI makes it easier to manage complex test scenarios.
Red team tests now include automatic token estimation for HTTP providers, helping you track costs even with custom API integrations.
A new System Prompt Override plugin tests whether your LLM deployment is vulnerable to system instruction manipulation—a critical security flaw that could disable safety features.
Real attacks rarely succeed in a single message. We've enhanced our attack strategies to better simulate how bad actors actually try to manipulate AI systems through extended, adaptive conversations:
Enhanced GOAT and Crescendo - Now include intelligent agents that can:
Emoji Encoding Strategy - New obfuscation technique using emoji to bypass content filters
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