Fixing Cloudflare Clearance and Release History Issues

TL;DR

We have patched a bug where Cloudflare clearance cookies were not being preserved during immediate target retries, causing unnecessary challenge loops. Additionally, we restored release history watermarks to prevent changelog regression and ensure monotonic PR processing.

Solving the Cloudflare Clearance Replay Bug

When scraping sites protected by advanced bot detection, the goal is to solve a challenge once and reuse that identity for the actual data request. In our previous implementation, the CloudflareSolver could successfully obtain a cf_clearance cookie, but the subsequent retry to the target URL often failed.

The Root Cause: Identity Mismatch

The issue existed within the worker logic handling the "fresh-solve" path. After the headed solver successfully cleared the challenge, the worker was handing a brand new browser cookie to the HTTP client for the target retry.

Because the clearance cookie is tied to a specific browser fingerprint and session identity, providing a "fresh" cookie from a different session invalidated the clearance. The target server saw a mismatch between the clearance cookie and the request identity, triggering a new managed challenge. This created a loop where the pipeline would eventually hit the T4 cap and return an unbilled failure, despite the solver reporting success.

The Technical Fix

We updated the CloudflareSolver to implement a browser-native solve-and-fetch result. Instead of separating the "solve" and "fetch" phases into two distinct identity contexts, the worker now preserves the exact browser identity that earned the clearance.

The immediate target retry now uses the same session state, ensuring the cf_clearance cookie is presented alongside the matching browser fingerprints. This fix applies to both GET and non-GET requests, ensuring that POST requests to protected endpoints also maintain their session identity.

For developers implementing their own anti-bot solution, this highlights the importance of session persistence. If you change your User-Agent or cookie jar between the solve and the fetch, the clearance will be revoked.

Implementing the Fix in Your Pipeline

If you were experiencing intermittent failures on high-tier targets, you no longer need to manually implement retry logic for clearance. You can simply call the /api/v1/scrape endpoint.

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "url": "https://target-site.com",
    "min_tier": 4
  }'

Restoring Release History and Generation Watermarks

Parallel to the solver fix, we identified a regression in our automated changelog generation system. This affected how we track and publish release notes for the AlterLab API.

The Watermark Regression

Our system uses a lastProcessedPR variable as a monotonic high-water mark. This ensures that the generator knows exactly which Pull Request was the last one included in a release, preventing the system from "rewinding" and re-processing old PRs.

In a recent staging deployment, the watermark rewound from 33576 to 33429. This caused two primary issues:

1. Data Loss: Published releases 5.0.6 and 5.0.7 were dropped from the artifact.
2. Deduplication Failure: The system began reconsidering PRs that had already been published, leading to potential duplicate entries in the changelog.

The Restoration Process

We have restored the promotion-base changelog artifact to ensure that releases 5.0.6 and 5.0.7 are preserved. More importantly, we have hard-coded the watermark back to 33576 and implemented a validation check to ensure the lastProcessedPR can only move forward.

Practical Application: Handling High-Tier Targets

When working with sites that utilize heavy bot detection, the most efficient path is to use a high min_tier to avoid the latency of escalating through T1–T3. By specifying the tier upfront, you trigger the CloudflareSolver logic immediately.

If you are using the Python SDK, your implementation should look like this:

import alterlab

client = alterlab.Client("YOUR_API_KEY")

# Use min_tier=4 to immediately engage the solver 
# and avoid T1-T3 escalation latency.
response = client.scrape(
    url="https://target-site.com", 
    min_tier=4
)

print(response.text)

Monitoring Your Success Rates

To verify that these fixes have improved your pipeline stability, we recommend monitoring your response codes. A successful fix for the clearance replay bug will manifest as a decrease in 403 Forbidden responses and a decrease in the number of times a request is escalated to the highest tier.

For detailed information on how different tiers affect your cost, refer to our API reference.

Takeaways

• Identity is Everything: Cloudflare clearance is tied to the browser session. Our worker now preserves this identity through the retry phase.
• Monotonicity Matters: In automated data pipelines, high-water marks (watermarks) are essential to prevent data duplication and history loss.
• Tier Optimization: Setting min_tier reduces latency and prevents unnecessary challenge loops on known protected targets.