- AISLE AI Toolkit Revealed OpenSSL Vulnerabilities Dating Back to the Earliest HTTPS Era
- Even heavily audited security code can hide serious flaws for decades
- Crashes and memory corruption remain common failure modes in cryptographic software
OpenSSL is one of the most widely used cryptographic libraries today, and forms the basis for HTTPS and encrypted communication across the Internet.
Despite decades of review, testing and community scrutiny, a coordinated January 2026 release addressed twelve previously undisclosed vulnerabilities.
These issues ranged from high and moderate severity bugs to a larger set of lower severity issues involving crashes, memory management errors, and encryption weaknesses.
Some of these deficiencies have persisted since 1998, highlighting the limits of human review even in heavily researched projects.
AISLE’s AI toolkit used context-aware detection to analyze OpenSSL’s code, assigning priority scores to potential threats and reducing false positives.
The autonomous system identified the twelve known CVEs and also discovered six additional issues prior to publication.
The most serious issue, CVE-2025-15467, involved a stack buffer overflow in CMS AuthEnvelopedData parsing, which under limited conditions could allow remote code execution.
A related but less serious bug, CVE-2025-11187, stemmed from missing parameter validation in PKCS#12 handling and created a path for stack-based buffer overflows with no guaranteed exploit.
Several vulnerabilities caused denial of service attacks through crashes or resource exhaustion rather than direct code execution.
CVE-2025-15468 caused crashes during QUIC encryption, CVE-2025-69420 affected TimeStamp Response verification, and CVE-2025-69421 caused errors during PKCS#12 decryption.
Similar crash behavior appeared in CVE-2026-22795, which was related to PKCS#12 parsing, and CVE-2026-22796, which disrupted PKCS#7 signature verification in legacy code paths.
Memory management errors formed another cluster of problems.
CVE-2025-66199 enabled memory consumption through TLS 1.3 certificate compression, which could degrade system availability.
CVE-2025-68160 exposed memory corruption in line buffer logic and affected versions dating back to OpenSSL 1.0.2.
A separate bug, tracked as CVE-2025-69419, involved memory corruption linked to PKCS#12 character encoding, although not all vulnerabilities caused immediate crashes or visible errors.
CVE-2025-15469 introduced silent truncation in post-quantum ML-DSA signature handling, which compromised cryptographic correctness without obvious runtime errors.
CVE-2025-69418 affected OCB encryption mode on hardware-accelerated paths and could weaken encryption guarantees under specific configurations.
These discoveries demonstrate that AI tools can operate continuously, examine all code paths at scale, and avoid limits related to time, attention, or code complexity.
Traditional static analysis tools often miss complex logic errors or time-dependent vulnerabilities, while autonomous analysis can uncover subtle errors.
By integrating directly into development workflows, the process resolved these findings before they impacted end users and demonstrated a level of coverage and speed far beyond manual review.
Working with OpenSSL maintainers, the AI-assisted process also recommended fixes, and maintainers incorporated some directly into OpenSSL’s code.
This shows that AI does not replace human expertise, but instead speeds up detection and remediation processes.
Endpoint protection measures and malware removal strategies can benefit from similar AI-driven approaches to identify hidden threats before deployment.
The AISLE results suggest that AI can shift cybersecurity from reactive patching to proactive protection.
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