The CompressionFilter class uses ZLib to deflate and inflate data sent and received. When we inflate incoming data, the filter does not control the resulting size, and create a buffer no matter what. Some compressed data may have a compression ration greater than 1 thousand, leading to an exhaustion of the application memory, as we don't control the deflated size. The fix adds such a control by allowing the application developer to provide a fixed size limit, which when reached throws an exception. It also allows the user to provide a compression ratio that should not be exceeded, protected the application from small inflated files that inflate in gigantic files, but with a grace limit for the resulting size (1Mb) to avoid false positive (like a very small file inflating with a high ratio, but resulting with a acceptable size, like a few thousands bytes) For application using this feature, it is highly recommended to create the CompressionFilter and to pass the maximum limit a
Casky was already ahead
This CVE exploits attack patterns that Casky's 0matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.
CVE-2026-47321 is a denial-of-service vulnerability in the CompressionFilter class that fails to validate decompressed data sizes during ZLib inflation operations. When processing incoming compressed data, the application allocates buffers without enforcing limits, allowing attackers to exploit compression ratios exceeding 1000:1 to rapidly exhaust system memory. This affects any application using vulnerable versions of the CompressionFilter for handling compressed network traffic, data streams, or file uploads. The vulnerability is particularly dangerous because it requires minimal attacker resources—a small compressed payload can trigger massive memory allocation—making it an effective vector for resource exhaustion attacks against web applications, APIs, and networked services.
While this CVE doesn't map directly to MITRE ATT&CK techniques, Casky's security skills library would help practitioners detect the attack patterns through reconnaissance of application resource behavior and impact analysis. Using Claude AI with extended reasoning across Casky's 754 mapped skills, practitioners would identify suspicious patterns such as: sudden spikes in memory consumption correlated with compressed data ingestion (indicating Resource Exhaustion attacks), application crashes or service unavailability following specific request patterns, and abnormal CPU usage from decompression operations. Security teams using Casky would receive findings highlighting the absence of decompression size controls, enabling them to prioritize patching efforts and implement compensating controls like request size limits and memory quotas before the vulnerability is actively exploited in the wild.
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