Reliability starts with separating instruction from content
Want to understand what real security risk looks like in generative AI? This 2025 case is a masterclass. Researchers at Aim Security found a zero-click vulnerability in Microsoft 365 Copilot, catalogued as CVE-2025-32711, CVSS 9.3. They named it EchoLeak. The attack required zero clicks from the victim. All it took was a malicious email landing in the inbox. When Copilot later processed that email as part of a user's query, hidden instructions in the body caused the model to pull sensitive data from other internal sources (email, SharePoint, OneDrive, Teams) and embed it in a URL, exfiltrating it to an external server. What stands out is how it slipped past the defenses. Microsoft's XPIA (Cross-Prompt Injection Attempt) classifier already existed. So did link redaction. The attack bypassed both using specific markdown formatting and an already-allowlisted Teams image proxy, so the traffic looked legitimate. Microsoft shipped a server-side fix in June 2025, with no user action required and no confirmed real-world exploitation. But the case exposed a structural problem: any AI assistant with access to multiple internal data sources has this same attack surface. The most interesting technical response Microsoft Research built for this is called Spotlighting. The idea is simple to explain and hard to implement well: the model needs to distinguish user instructions from untrusted external text. It does this three ways: delimiting with a randomized marker, datamarking by inserting a special character between words, or encoding the untrusted text in base64 or ROT13, always paired with a system prompt warning that the text is external. In tests with GPT-family models, Spotlighting dropped the indirect prompt injection attack success rate from over 50% to under 2%, without hurting task performance. It's now part of Prompt Shields, inside Azure AI Content Safety. If you work on AI projects that pull from multiple data sources, this is the kind of technical content that should be on your risk radar, not just on the security team's. Agent reliability starts with how it separates instruction from content it's merely processing.Microsoft's six Responsible AI pillars: how do you operationalize them in practice?
Deploying AI without a trust criterion isn't innovation. It's risk taken on without awareness. Microsoft organizes this problem into six pillars: reliability and safety, privacy and security, inclusiveness, transparency, accountability, and fairness. It's not a marketing list. It's a governance structure applicable to any AI adoption decision. The pillar that generates the most debate in practice, at least in the conversations I've had, is transparency. Systems that can't explain why they reached a given output create a real accountability problem: who signs off on the decision, the model or the manager? Accountability doesn't transfer to the tool. It stays with whoever decided to use it. Fairness also tends to be underestimated. Models trained on historical data carry the biases in that data. If the historical record was biased, the model replicates it at scale. Speed amplifies the problem, it doesn't fix it. Privacy and security are about where the data goes, who accesses it, what gets retained. Questions that need answers before deployment, not after an incident. The framework doesn't guarantee an outcome. It creates the right questions before any adoption decision. I'd like to hear how this plays out in practice for you all: which of these six pillars tends to be the hardest to operationalize on your team? And how are you documenting that decision process today?
Data Visualisation / Charting in Azure Foundry
Hi Foundry community, We are working on an agent that can query internal data sources, and are looking for ways that we can visualise data (think pie charts, bar charts, etc.). This would be consumed by end users through Copilot/Teams. However we are unable to find a way to do so, which is surprising given that you easily can create charts through M365 Copilot Chat and through Copilot Studio. We have tried using the 'Code Interpreter' tool, but the Teams/Copilot client UIs just do not render the results inline, either interactive or as an embedded image. They also do not give any option to download them. Has anyone tackled this before? How have you been able generate charts? Many thanks!
GPT-5.5-Pro not listed in foundry?
The model is mentioned in this blog post : https://azure.microsoft.com/en-us/blog/openais-gpt-5-5-in-microsoft-foundry-frontier-intelligence-on-an-enterprise-ready-platform/ But it is currently not listed on Foundry. Only latest pro model is 5.4-pro. When will 5.5-pro model be available on azure foundry?Home decor platform development
My customer is developing a home decor platform where users can upload images of their rooms, apply different decor items like furniture or wall colors, and visualize how they would look. The platform relies on AI tools and APIs for object recognition and placement but is facing issues with accurately identifying the floor and objects. The AI is currently rejecting the floor or placing items incorrectly. They are also working on improving search results based on uploaded images, which show similar but not exact matches, leading to inconsistencies in user experience. They are facing challenges in achieving accuracy and reducing processing time.
Import error: Cannot import name "PromptAgentDefinition" from "azure.ai.projects.models"
Hello, I am trying to build the agentic retrieval using Azure Ai search. During the creation of agent i am getting "ImportError: cannot import name 'PromptAgentDefinition' from 'azure.ai.projects.models'". Looked into possible ways of building without it but I need the mcp connection. This is the documentation i am following: https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-how-to-create-pipeline?tabs=search-perms%2Csearch-development%2Cfoundry-setup Note: There is no Promptagentdefinition in the directory of azure.ai.projects.models. ['ApiKeyCredentials', 'AzureAISearchIndex', 'BaseCredentials', 'BlobReference', 'BlobReferenceSasCredential', 'Connection', 'ConnectionType', 'CosmosDBIndex', 'CredentialType', 'CustomCredential', 'DatasetCredential', 'DatasetType', 'DatasetVersion', 'Deployment', 'DeploymentType', 'EmbeddingConfiguration', 'EntraIDCredentials', 'EvaluatorIds', 'FieldMapping', 'FileDatasetVersion', 'FolderDatasetVersion', 'Index', 'IndexType', 'ManagedAzureAISearchIndex', 'ModelDeployment', 'ModelDeploymentSku', 'NoAuthenticationCredentials', 'PendingUploadRequest', 'PendingUploadResponse', 'PendingUploadType', 'SASCredentials', 'TYPE_CHECKING', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_enums', '_models', '_patch', '_patch_all', '_patch_evaluations', '_patch_sdk'] Traceback (most recent call last): Please let me know what i should do and if there is any other alternative. Thanks in advance.
Deep Fake - what do u think about it ?
Hi, what do u think about deepfake technology ? I found this article https://jelesnianski.com/artificial-intelligence/before-you-believe-how-to-recognize-a-deepfake-and-is-it-inherently-evil/ Do you think that AI is more of a threat or an opportunity for development?
Is it possible to customize the automatically created webapp for chat?
Ive used AI studio to create an intelligent agent that answers questions on my content via the automatically created webapp (tutorial https://learn.microsoft.com/en-us/azure/ai-studio/tutorials/deploy-chat-web-app) Is it possible to customize this webapp, or use it as a base webpage and add additional features?
