Clear cases of False negative - incorrect identification of harmless content - From Azure OpenAI

Question

Clear cases of False negative - incorrect identification of harmless content - From Azure OpenAI

Venkatesan, Sangeetha 40

Lets start with why there is a need of content safety - Sure - we need to gaurd rail the input prompts being sent to the model - But it comes down to prevention of usability of system if there is consistent False negative - incorrect identification of harmless content - incorrectly flagging the prompts of user as Jail breaks. BadRequestError: Content Filter Triggered.

Consider I have a simple prompt - that generates a list of followup questions based on the user question, chat_history - very simple prompt.

I use GPT 40 MINI model and users send questions to the application. Almost 10 percent of calls end up in this false negative bucket.

Questions that needs clear explanation in the documentation:

How does the prompt then look like? if its always flagged as jail break in most essence
If there are always multilingual, some code based questions in prompt - I see a larger window of chances to be falsely tagged
For false negatives - retry is a quick band aid that app team can do, how does MSFT work on these false alarms and how can we have a realistic deterministic view towards the classification model [I can understand its probabilistic] - but here the predictions are very stochastic in nature.

Note: I have read all documentation related to Azure content filtering and Azure Content safety resource

Azar 29,520 Reputation points MVP Volunteer Moderator

2025-05-15T19:24:19.4833333+00:00

Hi there Venkatesan, Sangeetha

Thanks for using QandA platfrom

I've seen similar false positives (not false negatives) when using GPT-4o Mini in Azure, especially where simple prompts with multilingual input or embedded code get incorrectly flagged as jailbreak attempts

consider simplifying prompts and minimizing injected chat history where possible. Implementing retries is a practical workaround, but do log these cases they’re valuable for feedback. Also, Azure Content Safety can be used to pre-screen prompts if you're running into consistent blocks.

While we don't have direct control over the filtering thresholds**, Microsoft is actively refining these systems based on real-world feedback.**

If this helps kindly accept the answer thanks much.
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This comment has been deleted due to a violation of our Code of Conduct. The comment was manually reported or identified through automated detection before action was taken. Please refer to our Code of Conduct for more information.
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-15T19:56:23.11+00:00

Hi Venkatesan, Sangeetha

Content safety in Azure OpenAI is implemented through a sophisticated multi-layered filtering system that evaluates both prompts and responses for potentially harmful content. The system uses various categories and severity levels to determine whether content should be filtered, with categories including hate speech, self-harm content, sexual content, and violence detection.

The implementation of content safety is highly configurable, with severity thresholds that can be adjusted from "low" to "high" depending on your needs. While code examples are provided in the documentation to help implement these features, they are not mandatory - you can configure content safety through the Azure portal using Content Safety Studio or Azure AI Foundry's Content Safety page. The system is designed to be flexible, allowing you to either use the no-code approach through these interfaces or implement custom solutions using code if you prefer.

The content safety system is particularly important for applications like your chatbot, where you need to balance between maintaining safety and ensuring usability. While the system can sometimes generate false positives, especially with multilingual content and technical queries, these can be addressed through proper configuration and monitoring. The key is finding the right balance between safety and functionality, which can be achieved through either the code-based or no-code implementation approach, whichever best suits your development workflow and requirements.

The system is particularly relevant for chatbots, where multilingual content and technical queries may trigger incorrect flagging. Proper tuning and monitoring help mitigate false positives while maintaining safety.

Reference : Azure Open AI Service Content Filtering , Azure Content Safety
Thanks
Pavankumar Purilla 7,925 Reputation points Microsoft External Staff Moderator

2025-05-15T20:31:44.82+00:00

Hi Venkatesan, Sangeetha,
Ensuring content safety in AI models is essential to prevent harmful or inappropriate outputs. However, false negatives—where harmless content is mistakenly flagged as harmful—can affect usability. In Azure OpenAI, addressing false negatives requires a structured approach.

First, reviewing prompt structure is crucial. If a prompt is consistently flagged as a jailbreak or triggers content filtering, refining its wording can help. Clear language that avoids sensitive topics and aligns with Microsoft's guidelines reduces the likelihood of false negatives.

For multilingual and code-based queries, false negatives may arise due to misinterpretation of certain phrases or code snippets. Simplifying prompts or adding contextual information can mitigate this risk. Microsoft's content safety framework includes strategies for handling diverse input types.

When handling false negatives, retrying requests can be a temporary solution, but a more robust approach involves analyzing flagged prompts, identifying patterns, and refining prompt generation. Microsoft continuously enhances content filtering algorithms to improve classification accuracy and reduce false alarms.

Finally, achieving a realistic deterministic view is key. While AI models operate on probabilistic principles, Microsoft works to refine classification models, incorporate feedback mechanisms, and update filtering algorithms to minimize false negatives. Monitoring performance metrics and iteratively improving the system helps create a more reliable and deterministic classification process.

Reference : Azure Open AI Service Content Filtering , Azure Content Safety

I hope this information helps.
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-16T18:40:46.7533333+00:00

Hi Venkatesan, Sangeetha,

Just checking in to see if the above answer helped. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.
Thanks
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-19T21:47:23.79+00:00

Hi Venkatesan, Sangeetha,
Just following up to see if you had a chance to review the above response.

Thank you!
Venkatesan, Sangeetha 40 Reputation points

2025-05-20T17:22:20.5566667+00:00

I get it - but just that there is no clear definition of how to write a prompt that doesnt make these content filter systems flag - since I have just a simple followup questions generation prompt based on a given user question - wouldnt be a great deal to oversimplify the simple prompt which I already have! if there is a blog on how prompts should be written - any clear definition of traces or reasoning behind flagging would only be the most sustainable way of solving this problem!
Deleted

This comment has been deleted due to a violation of our Code of Conduct. The comment was manually reported or identified through automated detection before action was taken. Please refer to our Code of Conduct for more information.
Deleted

This comment has been deleted due to a violation of our Code of Conduct. The comment was manually reported or identified through automated detection before action was taken. Please refer to our Code of Conduct for more information.
SriLakshmi C 5,930 Reputation points Microsoft External Staff Moderator

2025-05-21T12:30:07.42+00:00

Hi Venkatesan, Sangeetha,

Microsoft does not provide a single definitive guide on writing prompts that completely avoid content filtering, as filtering decisions depend on multiple factors such as language nuances, context, and evolving AI safety standards. However, there are best practices to minimize false negatives while maintaining usability. Clarity and explicit intent in prompt wording help reduce misclassification, ensuring that the language used avoids ambiguous phrasing that could trigger filtering mechanisms.

Additionally, Microsoft's content filtering system flags prompts based on predefined categories, including hate, sexual content, violence, and self-harm. Structuring prompts to avoid triggering these categories can improve reliability. When dealing with multilingual or code-based queries, false negatives may arise due to misinterpretation of certain phrases or syntax. In such cases, providing additional context or adjusting wording can help mitigate filtering issues.

Microsoft's content safety framework continuously refines classification models to improve accuracy and minimize false alarms. While retrying requests may work temporarily, a more sustainable approach involves analyzing flagged prompts, identifying patterns, and refining generation techniques. Microsoft's ongoing improvements in AI classification systems support a more deterministic filtering process.

For further guidance, you can refer to Microsoft's Prompt Writing Guidelines offer best practices for crafting effective prompts while maintaining compliance with AI moderation policies.

I hope you understand. Do let me know if you have further queries.

Thank you!
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-22T18:26:35.75+00:00

Hi Venkatesan, Sangeetha,

Just checking in to see if the above answer helped. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Thanks
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-23T19:22:42.98+00:00

Hi Venkatesan, Sangeetha,

Following up to see if the above answer was helpful. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Thanks

Your answer

Azar 29,520 Reputation points MVP Volunteer Moderator

2025-05-15T19:24:19.4833333+00:00

Hi there Venkatesan, Sangeetha

Thanks for using QandA platfrom

I've seen similar false positives (not false negatives) when using GPT-4o Mini in Azure, especially where simple prompts with multilingual input or embedded code get incorrectly flagged as jailbreak attempts

consider simplifying prompts and minimizing injected chat history where possible. Implementing retries is a practical workaround, but do log these cases they’re valuable for feedback. Also, Azure Content Safety can be used to pre-screen prompts if you're running into consistent blocks.

While we don't have direct control over the filtering thresholds**, Microsoft is actively refining these systems based on real-world feedback.**

If this helps kindly accept the answer thanks much.
Deleted

This comment has been deleted due to a violation of our Code of Conduct. The comment was manually reported or identified through automated detection before action was taken. Please refer to our Code of Conduct for more information.
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-15T19:56:23.11+00:00

Hi Venkatesan, Sangeetha

Content safety in Azure OpenAI is implemented through a sophisticated multi-layered filtering system that evaluates both prompts and responses for potentially harmful content. The system uses various categories and severity levels to determine whether content should be filtered, with categories including hate speech, self-harm content, sexual content, and violence detection.

The implementation of content safety is highly configurable, with severity thresholds that can be adjusted from "low" to "high" depending on your needs. While code examples are provided in the documentation to help implement these features, they are not mandatory - you can configure content safety through the Azure portal using Content Safety Studio or Azure AI Foundry's Content Safety page. The system is designed to be flexible, allowing you to either use the no-code approach through these interfaces or implement custom solutions using code if you prefer.

The content safety system is particularly important for applications like your chatbot, where you need to balance between maintaining safety and ensuring usability. While the system can sometimes generate false positives, especially with multilingual content and technical queries, these can be addressed through proper configuration and monitoring. The key is finding the right balance between safety and functionality, which can be achieved through either the code-based or no-code implementation approach, whichever best suits your development workflow and requirements.

The system is particularly relevant for chatbots, where multilingual content and technical queries may trigger incorrect flagging. Proper tuning and monitoring help mitigate false positives while maintaining safety.

Reference : Azure Open AI Service Content Filtering , Azure Content Safety
Thanks
Pavankumar Purilla 7,925 Reputation points Microsoft External Staff Moderator

2025-05-15T20:31:44.82+00:00

Hi Venkatesan, Sangeetha,
Ensuring content safety in AI models is essential to prevent harmful or inappropriate outputs. However, false negatives—where harmless content is mistakenly flagged as harmful—can affect usability. In Azure OpenAI, addressing false negatives requires a structured approach.

First, reviewing prompt structure is crucial. If a prompt is consistently flagged as a jailbreak or triggers content filtering, refining its wording can help. Clear language that avoids sensitive topics and aligns with Microsoft's guidelines reduces the likelihood of false negatives.

For multilingual and code-based queries, false negatives may arise due to misinterpretation of certain phrases or code snippets. Simplifying prompts or adding contextual information can mitigate this risk. Microsoft's content safety framework includes strategies for handling diverse input types.

When handling false negatives, retrying requests can be a temporary solution, but a more robust approach involves analyzing flagged prompts, identifying patterns, and refining prompt generation. Microsoft continuously enhances content filtering algorithms to improve classification accuracy and reduce false alarms.

Finally, achieving a realistic deterministic view is key. While AI models operate on probabilistic principles, Microsoft works to refine classification models, incorporate feedback mechanisms, and update filtering algorithms to minimize false negatives. Monitoring performance metrics and iteratively improving the system helps create a more reliable and deterministic classification process.

Reference : Azure Open AI Service Content Filtering , Azure Content Safety

I hope this information helps.
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-16T18:40:46.7533333+00:00

Hi Venkatesan, Sangeetha,

Just checking in to see if the above answer helped. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.
Thanks
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-19T21:47:23.79+00:00

Hi Venkatesan, Sangeetha,
Just following up to see if you had a chance to review the above response.

Thank you!
Venkatesan, Sangeetha 40 Reputation points

2025-05-20T17:22:20.5566667+00:00

I get it - but just that there is no clear definition of how to write a prompt that doesnt make these content filter systems flag - since I have just a simple followup questions generation prompt based on a given user question - wouldnt be a great deal to oversimplify the simple prompt which I already have! if there is a blog on how prompts should be written - any clear definition of traces or reasoning behind flagging would only be the most sustainable way of solving this problem!
Deleted

This comment has been deleted due to a violation of our Code of Conduct. The comment was manually reported or identified through automated detection before action was taken. Please refer to our Code of Conduct for more information.
SriLakshmi C 5,930 Reputation points Microsoft External Staff Moderator

2025-05-21T12:30:07.42+00:00

Hi Venkatesan, Sangeetha,

Microsoft does not provide a single definitive guide on writing prompts that completely avoid content filtering, as filtering decisions depend on multiple factors such as language nuances, context, and evolving AI safety standards. However, there are best practices to minimize false negatives while maintaining usability. Clarity and explicit intent in prompt wording help reduce misclassification, ensuring that the language used avoids ambiguous phrasing that could trigger filtering mechanisms.

Additionally, Microsoft's content filtering system flags prompts based on predefined categories, including hate, sexual content, violence, and self-harm. Structuring prompts to avoid triggering these categories can improve reliability. When dealing with multilingual or code-based queries, false negatives may arise due to misinterpretation of certain phrases or syntax. In such cases, providing additional context or adjusting wording can help mitigate filtering issues.

Microsoft's content safety framework continuously refines classification models to improve accuracy and minimize false alarms. While retrying requests may work temporarily, a more sustainable approach involves analyzing flagged prompts, identifying patterns, and refining generation techniques. Microsoft's ongoing improvements in AI classification systems support a more deterministic filtering process.

For further guidance, you can refer to Microsoft's Prompt Writing Guidelines offer best practices for crafting effective prompts while maintaining compliance with AI moderation policies.

I hope you understand. Do let me know if you have further queries.

Thank you!
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-22T18:26:35.75+00:00

Hi Venkatesan, Sangeetha,

Just checking in to see if the above answer helped. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Thanks
Ravada Shivaprasad 535 Reputation points Microsoft External Staff Moderator

2025-05-23T19:22:42.98+00:00

Hi Venkatesan, Sangeetha,

Following up to see if the above answer was helpful. If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Thanks

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Clear cases of False negative - incorrect identification of harmless content - From Azure OpenAI

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