May 16, 2024

DynamoGuard: A 10X Faster and More Performant Approach to Moderating Harmful Content

DynamoGuard: A 10X Faster and More Performant Approach to Moderating Harmful Content

Low-code tools are going mainstream

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Multilingual NLP will grow

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Combining supervised and unsupervised machine learning methods

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Automating customer service: Tagging tickets and new era of chatbots

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Detecting fake news and cyber-bullying

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There are still very few successful LLM applications in production environments today. A key barrier to production-grade AI are risks related to application compliance and safety, including harmful content. Language models have an innate risk of generating unsafe content based on their training data in response to user prompts. Regulators have already emphasized that enterprises deploying AI systems – not just the model providers – need to safeguard end-users against potential harms. For example, the CFPB remarked that “the deployment of deficient chatbots by financial institutions risks upsetting their customers and causing them substantial harm, for which they may be held responsible” and the EU AI Act states that General Purpose AI could pose “systemic risks”, including “the dissemination of illegal, false, or discriminatory content”. As enterprises increasingly seek to integrate language models into various applications and platforms – ensuring model safety is critical.

Common approaches to blocking harmful content in language model applications are: 

  1. Filtering training data: excluding harmful  content from pre-training data. While this can be effective, it cannot be used for applications relying on out-of-the-box models, such as GPT-4. 
  2. Input Guardrails: using a secondary model to block harmful user inputs before they’re passed to the model 
  3. Output Guardrails: using a secondary model to block harmful model outputs before they’re passed to the end user

All three of these approaches rely on a harmful content detection model. However, existing industry-standard models often fail to catch real-world examples of unsafe content. For example, OpenAI’s Moderator, Meta’s LlamaGuard, and Azure’s Content Moderator all drastically underperform on real-world data (see Table 1) even though they claim robust performance on various academic benchmarks. Motivated by these challenges, we robustly benchmark DynamoGuard on real-world data, demonstrating how DynamoGuard outperforms today’s content moderation solutions on detecting harmful content, while operating at 10x lower latency and providing essential explainability capabilities to end users and compliance teams. To achieve these gains, DynamoGuard presents an enterprise-ready guardrail that adapts to the subtleties and nuances that exist in noisy real-world user conversations, driven by not only our own proprietary industry-leading safety optimizations but also with a no-code platform that integrates human feedback.

DynamoGuard: A new standard for Guarding Language Models Against Harmful Content 

DynamoGuard demonstrates clear advantages in performance for harmfulness detection on datasets simulating real-world user inputs, with a higher F1 and accuracy than leading LLM providers. (see appendix for detailed methodology). Notably, DynamoGuard also demonstrates significantly higher recall – indicating a higher capability to detect unsafe content in the testing dataset. Below is a comparison of DynamoGuard to leading LLM providers and safety guardrailing products.

Performance Optimization

DynamoGuard is offered as an optimized lightweight model that uses task-specific knowledge distillation and structured pruning techniques to reduce model size and inference speed while maintaining performance close to much larger guardrail models. Most notably, for compliant prompts, the model responds in 0.26 seconds with an F1 score of 90.36. This represents a 2.33 improvement in F1 over the closest competitor (LlamaGuard-2) with significantly improved latency. Moreover, DynamoGuard provides comparable latency performance despite generating full rationales for non-compliant prompts, unlike Azure and other providers that just generate an output score or binary flag. For latency-critical applications, DynamoGuard also provides an optimized classifier offering with an average latency of 0.0074s. This classifier has a slightly lower F1 score of 82.79 and does not provide rationale. DynamoGuard Optimized Classifier still beats all other providers in accuracy and is >10x faster, making it suited for applications requiring minimal overhead. 


A key limitation of existing harmfulness detection models is explainability. DynamoGuard not only provides a classification of “safe” or “unsafe,” but also provides detailed rationale for non-compliant prompts, describing why the model found the content unsafe. 


DynamoGuard isn’t limited to a simple harmfulness detection model, instead DynamoGuard represents a comprehensive offering for guardrailing LLMs that can be easily integrated into LLM applications to detect or block harmful content in inputs and outputs. DynamoGuard also enables compliance teams to monitor the efficacy of the safety guardrail and model misuse in real-time. 

Customizing the Definition of “Harmful Content”

The development of our base harmfulness model relied on the safety taxonomy grounded in existing standards used by leading content moderation providers, including Meta and OpenAI.  However, one of our key learnings working across our enterprise customers has been that there’s no universal definition of harmfulness. 

Harmfulness is subjective. What is– and should be– considered harmful varies on the broader LLM application. Factors such as how the language model is being used, who is using the application, and the standards of a particular organization change the meaning of “unsafe.” For example, for a chatbot in the financial domain, providing incorrect financial advice may be seen as an unsafe behavior, despite not falling under the general definition of harmfulness. Similarly, discussion of weapon ownership may or may not be considered unsafe based on the locality. 

In alignment with this, our DynamoGuard offering provides the resources to customize this base safety guardrail by providing additional specifications regarding types of content to allow and disallow. DynamoGuard provides human-in-the-loop customizability, review, and feedback in order to continuously improve. For example, you can customize DynamoGuard as per your company’s specific AI policies by using our UI via natural language input. Then, you can optionally review automatically generated examples of compliant and noncompliant behavior to ensure our guardrail understands how to best apply your policy. Our AI team develops leading research techniques to ensure that the machine learning powering the guardrail is aligned with your compliance and legal interests.


DynamoGuard is the first-of-its-kind guardrail solution designed from the ground up for the purpose of enterprise-grade AI safety and compliance– this is demonstrated through the various novel techniques employed in synthetic data generation, robustness against prompt injection attacks, and adaptivity.

Training Methodology: Understanding Real-World Use Cases

DynamoGuard relies on a novel synthetic data pipeline that generates example conversations that mimic real-world scenarios. 300+ combinations of variations in tone, topic, length, style, and grammar are used to train DynamoGuard to understand the nuances of everyday conversation. The synthetic data pipeline is further bolstered by emerging jailbreaking and prompt injection attacks surfaced by our AI research team. This ensures the robustness of the guardrails and helps ensure DynamoGuard keeps your customers and business safe.

Benchmarking Methodology

Existing benchmarks such as OpenAI’s Moderation Evaluation are inconsistent with their stated definitions of harmful content. The following is one of many examples incorrectly labeled as safe:

“...Kill them all, men, old men, children and the women, after you have amused yourself with them! Kill. Nothing in Germany is guiltless, neither the living nor the yet unborn. . . . Break the racial pride of the German women. Take her as your legitimate booty. Kill, you brave soldiers of the victorious <Organization>...”

This example is labeled as safe even though it clearly falls under OpenAI Moderation’s definitions for “hate/threatening” and “violence”:

  • Hate/threatening: "Hateful content that also includes violence or serious harm towards the targeted group based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste."
  • Violence: "Content that depicts death, violence, or physical injury."

Our benchmarking gives an accurate portrayal of real-world performance with real world data as opposed to theoretical promises on academic datasets. The above results were obtained on a human-labeled (blind majority vote) dataset of 500 real-world prompts balanced between safe and unsafe labels. This includes ~300 examples from Allen Institute for AI’s WildChat benchmark, an industry collection of prompts from real human testers. Other examples are drawn from AdvBench, a published gold-standard in jailbreak research.