- AISLE AI toolset exposed OpenSSL vulnerabilities dating back to early HTTPS era
- Even heavily audited security code can hide serious flaws for decades
- Failures and memory corruption remain common failure modes in crypto software
OpenSSL is one of the most widely deployed cryptographic libraries today and forms the basis of HTTPS and encrypted communications over the Internet.
Despite decades of review, testing, and community scrutiny, a coordinated January 2026 release fixed twelve previously undisclosed vulnerabilities.
These issues ranged from high and moderate severity flaws to a broader set of lower severity issues involving crashes, memory management errors, and encryption weaknesses.
Some of these flaws persisted since 1998, highlighting the limits of human evaluation, even in highly scrutinized projects.
AISLE’s AI toolset used contextual detection to analyze OpenSSL’s code, assign priority scores to potential threats, and reduce false positives.
The autonomous system identified all twelve known CVEs and also detected six additional issues before their public disclosure.
The most serious issue, CVE-2025-15467, involved a stack buffer overflow in the CMS AuthEnvelopedData parsing, which, under constrained conditions, could allow remote code execution.
A related but less serious flaw, CVE-2025-11187, stemmed from missing parameter validation in PKCS#12 handling and created a path for a stack-based buffer overflow with no guaranteed exploitability.
Several vulnerabilities caused denial of service conditions via crashes or resource exhaustion rather than direct code execution.
CVE-2025-15468 triggered crashes when handling QUIC encryption, CVE-2025-69420 affected TimeStamp response checking, and CVE-2025-69421 caused failures during PKCS#12 decryption.
Similar crash behavior appeared in CVE-2026-22795, related to PKCS#12 parsing, and CVE-2026-22796, which disrupted PKCS#7 signature verification in existing code paths.
Memory management errors were another group of problems.
CVE-2025-66199 allowed memory exhaustion via compression of TLS 1.3 certificates, which could degrade system availability.
CVE-2025-68160 revealed memory corruption in line buffering logic and affected versions dating back to OpenSSL 1.0.2.
A separate flaw, identified as CVE-2025-69419, involved memory corruption related to PKCS#12 character encoding, although not all vulnerabilities caused immediate crashes or visible defects.
CVE-2025-15469 introduced silent truncation in post-quantum handling of ML-DSA signatures, which put cryptographic correctness at risk without obvious runtime errors.
CVE-2025-69418 affected OCB encryption mode on hardware-accelerated paths and could weaken encryption guarantees in specific configurations.
These findings show that AI tools can operate continuously, examine all code paths at scale, and avoid limitations related to time, attention, or code complexity.
Traditional static analysis tools often overlook complex logical errors or timing-dependent vulnerabilities, while standalone analysis can reveal subtle flaws.
By integrating directly into development workflows, the process resolved these issues 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 adopted some directly into OpenSSL code.
This shows that AI does not replace human expertise but instead accelerates detection and remediation processes.
Endpoint protection measures and malware removal strategies can benefit from similar AI-driven approaches to identify hidden threats before deployment.
AISLE results suggest that AI can shift cybersecurity from reactive patching to proactive protection.
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