Tell me about a time where you disagree with someone.
production experience
project CBT
what’s about
“One project I’m particularly proud of involved improving the security of desktop single sign-on authentication flows in Microsoft’s distributed identity infrastructure.
The issue involved a potential man-in-the-middle risk where Kerberos authentication tickets could potentially be relayed across connections if the backend could not verify that the authentication occurred over the original trusted TLS channel.
My work focused on implementing Channel Binding Token validation across gateway and backend authentication services to strengthen trust validation between transport-layer authentication and application-layer ticket validation.
One of the main challenges was balancing stronger security enforcement with compatibility across large enterprise environments, since different customers had different client configurations and TLS behaviors.
In addition to implementing the validation flow, I worked on safe rollout considerations, backend enforcement behavior, and observability to ensure we could monitor failures without accidentally disrupting legitimate enterprise authentication traffic.”
CBT
“CBT allows the backend to verify that the Kerberos authentication happened over the same TLS channel originally established by the client, preventing attackers from relaying authentication across different connections.”
how safely rollout
We had to be careful not to introduce authentication regressions at scale, so rollout safety and observability were very important. Monitoring validation failures and understanding environment-specific behaviors were critical before stronger enforcement could be applied broadly.
what did you learn
The project taught me that security engineering in large distributed systems is often more about rollout safety, compatibility, and trust-boundary validation than simply implementing cryptographic checks.
how to improve
“I think one thing we could improve further is observability around authentication failures caused by CBT mismatch.
At the time, we mainly focused on enforcing validation safely, but if I revisit the project now, I would invest more in:
- structured telemetry
- failure categorization
- gradual policy rollout
- automated anomaly detection
because security enforcement in enterprise systems often fails due to edge-case environments rather than implementation bugs.”
project KKRR
what’s about
“One authentication reliability issue I worked on involved Kerberos key rotation in hybrid enterprise environments.
The problem was that after a customer rotated their on-premises Kerberos key, there was a short transition window where previously-issued tickets encrypted with the old key were still valid and actively used by clients, while the service had already switched to accepting only the newly-rotated key.
This created temporary authentication failures because valid in-flight tickets could no longer be decrypted successfully, leading to sign-in outages during the propagation window.
The challenge was balancing security and availability.
We could not simply keep old keys indefinitely because that weakens key hygiene and increases security exposure, but immediately enforcing only the new key caused availability issues for enterprise customers.
My solution was implementing a dual-key decryption strategy during the rotation transition period.
Instead of validating against only the latest Kerberos principal key, the authentication service temporarily maintained both:
- the newly-rotated active key
- and the previous key within a bounded transition window
During ticket validation, the service first attempted decryption using the new key, and if that failed, retried using the previous key.
This allowed previously-issued tickets to remain functional until natural expiration, eliminating transient outages during key propagation and replication delays.
One important consideration was ensuring the fallback path remained time-bounded and observable, so we added telemetry around old-key fallback usage to monitor rotation convergence and prevent indefinite legacy-key dependency.”
how to do better
“If I revisit this project now, I think the biggest improvement area would be around observability and automated rotation coordination rather than the decryption logic itself.
At the time, the primary goal was preventing temporary authentication outages during Kerberos key rotation, and the dual-key fallback mechanism solved that reliability issue effectively.
But looking back, I think we could further improve the system in several areas.
First, I would add more detailed telemetry around old-key fallback usage:
- fallback frequency
- customer-level distribution
- ticket age patterns
- rotation convergence timing
because this would help us understand whether some environments were consistently lagging behind during key propagation.
Second, I would improve rollout coordination across distributed services.
One challenge in hybrid enterprise systems is that key propagation timing is not always deterministic, especially across on-prem and cloud boundaries.
A more coordinated rotation state machine or staged rollout approach could reduce the transition window further.
Third, I would probably add automatic stale-key convergence monitoring.
For example, if the system detects old-key fallback usage persisting beyond expected ticket lifetime, it could proactively alert operators about replication or configuration issues.
One thing I learned from this project is that reliability problems in distributed authentication systems are often less about the cryptographic logic itself and more about handling timing, propagation, and operational visibility safely at scale.”
“I would also think more carefully about minimizing the fallback exposure window.
Although dual-key validation improves availability significantly, there is always a tradeoff between backward compatibility and strict key hygiene.
If redesigning today, I might explore:
- shorter bounded fallback windows
- adaptive expiration policies
- or stronger coordination with ticket issuance timing
to further reduce old-key exposure while still maintaining seamless customer experience.”
Aaguid concurrent bug
“One of the most interesting bugs I uncovered involved a telemetry discrepancy on our executive dashboard for passwordless authentication.
We were tracking the adoption rates of two major features: Passkeys and Windows Hello for Business (WHfB). We noticed that the Passkey usage numbers were suspiciously high, while Windows Hello numbers were dropping. Because these metrics directly influenced our team’s product strategy, I volunteered to investigate why the data looked so warped.
To differentiate between a Passkey and a Windows Hello login, our pipeline inspected a unique identifier called an AAGUID (Authenticator Attestation Global Unique Identifier). Passkeys always have one, while standard Windows Hello logins typically do not.
I dug into the backend telemetry processor and started debugging how these logs were being parsed. That’s when I uncovered a classic multi-threading concurrency issue.
The processing logic was using a shared utility class with a static field to hold the parsed request metadata. Because our service handles thousands of requests concurrently, what was happening was a race condition and a data leak between threads.
If request A was a Passkey login, the static field was populated with its AAGUID. If request B—which came immediately after on a different thread—was a Windows Hello login, the shared utility didn’t explicitly clear or overwrite that static field. As a result, the Windows Hello request ‘inherited’ the previous request’s AAGUID, and the system incorrectly logged it as a Passkey.
To fix this, I refactored the utility class to eliminate the shared static state entirely. I modified the pipeline to use thread-local variables and properly encapsulated the request context, ensuring that every authentication request had its own isolated, short-lived data object that was safely garbage-collected. I also wrote a suite of concurrent unit tests to simulate rapid, back-to-back requests to prove the leak was gone.
The impact was immediate. Once we deployed the fix, the telemetry self-corrected, and we gave leadership an accurate, reliable dashboard for product adoption.
It was a great reminder for me that when you are working at cloud scale, statelessness and thread-safety aren’t just best practices—they are absolutely critical. A single misplaced static keyword can corrupt data for millions of users.”