CVE-2026-85228 - Integer overflow in tensor buffer validation in Deep Java Library
CVE-2026-85228 is an integer overflow vulnerability in the tensor buffer validation component of the Deep Java Library (DJL), an open-source Java framework for deep learning maintained by Amazon. The flaw occurs when a crafted tensor payload declares a shape whose computed byte size exceeds the 32-bit signed integer range, causing the size to wrap and allowing an undersized buffer to pass validation. This leads to out-of-bounds reads during tensor operations. Exploitation could allow a remote, unauthenticated attacker to access adjacent process memory or cause a denial of service. A fix has been released in DJL version 0.37.0. Users are advised to upgrade to this version or later. No workaround other than upgrading is available, but limiting tensor input to trusted sources can reduce risk until patched.
AI Analysis
Technical Summary
The vulnerability CVE-2026-85228 in Deep Java Library (DJL) involves an integer overflow in tensor buffer validation across all platforms. When a tensor payload specifies a shape whose computed byte size exceeds the 32-bit signed integer limit, the size calculation wraps around, allowing an undersized buffer to pass validation. Subsequent tensor operations then perform out-of-bounds reads, potentially exposing adjacent process memory or causing denial of service. This can be exploited remotely without authentication. AWS has released a fix in DJL version 0.37.0 and recommends upgrading from affected versions 0.13.0 through 0.36.0. No alternative mitigations exist beyond upgrading, but restricting input to trusted sources is advised until patched.
Potential Impact
A remote, unauthenticated attacker can exploit this integer overflow to read memory beyond the intended buffer boundaries, potentially leaking sensitive information from adjacent process memory. Additionally, the vulnerability can cause denial of service by triggering out-of-bounds memory access. This impacts confidentiality and availability of applications using vulnerable versions of DJL.
Mitigation Recommendations
An official fix is available in Deep Java Library version 0.37.0. Users should upgrade to version 0.37.0 or later to remediate this vulnerability. There are no workarounds other than upgrading. Until the upgrade is applied, it is recommended to only accept tensor input from trusted sources and avoid exposing raw-tensor (binary mode) inference endpoints backed by native Java engines to untrusted callers.
CVE-2026-85228 - Integer overflow in tensor buffer validation in Deep Java Library
Description
CVE-2026-85228 is an integer overflow vulnerability in the tensor buffer validation component of the Deep Java Library (DJL), an open-source Java framework for deep learning maintained by Amazon. The flaw occurs when a crafted tensor payload declares a shape whose computed byte size exceeds the 32-bit signed integer range, causing the size to wrap and allowing an undersized buffer to pass validation. This leads to out-of-bounds reads during tensor operations. Exploitation could allow a remote, unauthenticated attacker to access adjacent process memory or cause a denial of service. A fix has been released in DJL version 0.37.0. Users are advised to upgrade to this version or later. No workaround other than upgrading is available, but limiting tensor input to trusted sources can reduce risk until patched.
Affected software
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AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The vulnerability CVE-2026-85228 in Deep Java Library (DJL) involves an integer overflow in tensor buffer validation across all platforms. When a tensor payload specifies a shape whose computed byte size exceeds the 32-bit signed integer limit, the size calculation wraps around, allowing an undersized buffer to pass validation. Subsequent tensor operations then perform out-of-bounds reads, potentially exposing adjacent process memory or causing denial of service. This can be exploited remotely without authentication. AWS has released a fix in DJL version 0.37.0 and recommends upgrading from affected versions 0.13.0 through 0.36.0. No alternative mitigations exist beyond upgrading, but restricting input to trusted sources is advised until patched.
Potential Impact
A remote, unauthenticated attacker can exploit this integer overflow to read memory beyond the intended buffer boundaries, potentially leaking sensitive information from adjacent process memory. Additionally, the vulnerability can cause denial of service by triggering out-of-bounds memory access. This impacts confidentiality and availability of applications using vulnerable versions of DJL.
Mitigation Recommendations
An official fix is available in Deep Java Library version 0.37.0. Users should upgrade to version 0.37.0 or later to remediate this vulnerability. There are no workarounds other than upgrading. Until the upgrade is applied, it is recommended to only accept tensor input from trusted sources and avoid exposing raw-tensor (binary mode) inference endpoints backed by native Java engines to untrusted callers.
Technical Details
- Classification
- {"confidence":0.93,"severitySource":"heuristic","classifier":"rss-v2"}
- Article Source
- {"url":"https://aws.amazon.com/security/security-bulletins/rss/2026-106-aws/","fetched":true,"fetchedAt":"2026-09-10T17:19:59.878Z","wordCount":243}
Threat ID: 6aa2e6bff76d26f02560a6ee
Added to database: 09/10/2026, 17:19:59 UTC
Last enriched: 09/10/2026, 17:20:06 UTC
Last updated: 09/10/2026, 17:51:26 UTC
Views: 7
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