https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Mon, 12 Oct 2026 23:42:54 +0000 https://cve.radiocsirt.org/vuln/euvd-2026-273600 EUVD-2026-273600 https://cve.radiocsirt.org/vuln/euvd-2026-273600 https://cve.radiocsirt.org/vuln/fkie_cve-2026-27695 <p>zae-limiter is a rate limiting library using the token bucket algorithm. Prior to version 0.10.1, all rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB&#39;s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition. Version 0.10.1 fixes the issue.</p> <p>zae-limiter is a rate limiting library using the token bucket algorithm. Prior to version 0.10.1, all rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB&#39;s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition. Version 0.10.1 fixes the issue.</p> https://cve.radiocsirt.org/vuln/fkie_cve-2026-27695 https://cve.radiocsirt.org/vuln/ghsa-76rv-2r9v-c5m6 <p><strong>Affected:</strong> PyPI: zae-limiter</p> <p>## Summary</p> <p>All rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB&#39;s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition.</p> <p>## Details</p> <p>Each `acquire()` call performs a `TransactWriteItems` (or `UpdateItem` in speculative mode) against items sharing the same partition key. For cascade entities, this doubles to 2-4 writes per request (child + parent). At sustained rates above ~500 req/sec for a single entity, DynamoDB&#39;s adaptive capacity may not redistribute fast enough, causing `ProvisionedThroughputExceededException`.</p> <p>The library has no built-in mitigation: - No partition key sharding/salting - No write coalescing or batching - No client-side admission control before hitting DynamoDB - `RateLimiterUnavailable` is raised but the caller has already been delayed</p> <p>## Impact</p> <p>- **Availability**: High-traffic entities experience elevated latency and rejected requests beyond what their rate limits specify - **Fairness**: Other entities sharing the same DynamoDB partition may experience collateral throttling - **Multi-tenant risk**: In a shared LLM proxy scenario, one tenant&#39;s burst traffic could degrade service for others</p> <p>## Reproduction</p> <p>1. Create an entity with high rate limits (e.g., 100,000 rpm) 2. Send sustained traffic at 1,000+ req/sec to a single entity 3. Observe D…</p> <p><strong>Affected:</strong> PyPI: zae-limiter</p> <p>## Summary</p> <p>All rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB&#39;s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition.</p> <p>## Details</p> <p>Each `acquire()` call performs a `TransactWriteItems` (or `UpdateItem` in speculative mode) against items sharing the same partition key. For cascade entities, this doubles to 2-4 writes per request (child + parent). At sustained rates above ~500 req/sec for a single entity, DynamoDB&#39;s adaptive capacity may not redistribute fast enough, causing `ProvisionedThroughputExceededException`.</p> <p>The library has no built-in mitigation: - No partition key sharding/salting - No write coalescing or batching - No client-side admission control before hitting DynamoDB - `RateLimiterUnavailable` is raised but the caller has already been delayed</p> <p>## Impact</p> <p>- **Availability**: High-traffic entities experience elevated latency and rejected requests beyond what their rate limits specify - **Fairness**: Other entities sharing the same DynamoDB partition may experience collateral throttling - **Multi-tenant risk**: In a shared LLM proxy scenario, one tenant&#39;s burst traffic could degrade service for others</p> <p>## Reproduction</p> <p>1. Create an entity with high rate limits (e.g., 100,000 rpm) 2. Send sustained traffic at 1,000+ req/sec to a single entity 3. Observe D…</p> https://cve.radiocsirt.org/vuln/ghsa-76rv-2r9v-c5m6 https://cve.radiocsirt.org/vuln/pysec-2026-3434 <p><strong>Affected:</strong> PyPI: zae-limiter</p> <p>## Summary</p> <p>All rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB&#39;s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition.</p> <p>## Details</p> <p>Each `acquire()` call performs a `TransactWriteItems` (or `UpdateItem` in speculative mode) against items sharing the same partition key. For cascade entities, this doubles to 2-4 writes per request (child + parent). At sustained rates above ~500 req/sec for a single entity, DynamoDB&#39;s adaptive capacity may not redistribute fast enough, causing `ProvisionedThroughputExceededException`.</p> <p>The library has no built-in mitigation: - No partition key sharding/salting - No write coalescing or batching - No client-side admission control before hitting DynamoDB - `RateLimiterUnavailable` is raised but the caller has already been delayed</p> <p>## Impact</p> <p>- **Availability**: High-traffic entities experience elevated latency and rejected requests beyond what their rate limits specify - **Fairness**: Other entities sharing the same DynamoDB partition may experience collateral throttling - **Multi-tenant risk**: In a shared LLM proxy scenario, one tenant&#39;s burst traffic could degrade service for others</p> <p>## Reproduction</p> <p>1. Create an entity with high rate limits (e.g., 100,000 rpm) 2. Send sustained traffic at 1,000+ req/sec to a single entity 3. Observe D…</p> <p><strong>Affected:</strong> PyPI: zae-limiter</p> <p>## Summary</p> <p>All rate limit buckets for a single entity share the same DynamoDB partition key (`namespace/ENTITY#{id}`). A high-traffic entity can exceed DynamoDB&#39;s per-partition throughput limits (~1,000 WCU/sec), causing throttling that degrades service for that entity — and potentially co-located entities in the same partition.</p> <p>## Details</p> <p>Each `acquire()` call performs a `TransactWriteItems` (or `UpdateItem` in speculative mode) against items sharing the same partition key. For cascade entities, this doubles to 2-4 writes per request (child + parent). At sustained rates above ~500 req/sec for a single entity, DynamoDB&#39;s adaptive capacity may not redistribute fast enough, causing `ProvisionedThroughputExceededException`.</p> <p>The library has no built-in mitigation: - No partition key sharding/salting - No write coalescing or batching - No client-side admission control before hitting DynamoDB - `RateLimiterUnavailable` is raised but the caller has already been delayed</p> <p>## Impact</p> <p>- **Availability**: High-traffic entities experience elevated latency and rejected requests beyond what their rate limits specify - **Fairness**: Other entities sharing the same DynamoDB partition may experience collateral throttling - **Multi-tenant risk**: In a shared LLM proxy scenario, one tenant&#39;s burst traffic could degrade service for others</p> <p>## Reproduction</p> <p>1. Create an entity with high rate limits (e.g., 100,000 rpm) 2. Send sustained traffic at 1,000+ req/sec to a single entity 3. Observe D…</p> https://cve.radiocsirt.org/vuln/pysec-2026-3434