CVE-2026-6786
NixOS vulnerability analysis and mitigation

Overview

CVE-2026-6786 is a memory safety vulnerability affecting Mozilla Firefox and Thunderbird, involving multiple memory corruption bugs including out-of-bounds write (CWE-787), use-after-free (CWE-416), and out-of-bounds read (CWE-125) conditions. It affects Firefox ESR 140.9, Thunderbird ESR 140.9, Firefox 149, and Thunderbird 149. The vulnerability was disclosed on April 21, 2026, and fixed in Firefox 150, Firefox ESR 140.10, and corresponding Thunderbird versions. It carries a CVSS v3.1 base score of 7.5 (High) per Feedly/Red Hat, and 8.1 (High) per ENISA (Mozilla MFSA2026-30, Mozilla MFSA2026-32).

Technical details

The vulnerability stems from multiple memory safety bugs discovered through fuzzing and manual review by Mozilla engineers including Alex Franchuk, Andrew McCreight, Brian Grinstead, Christian Holler, Jan de Mooij, Maurice Dauer, Sebastian Hengst, and Tom Schuster. The root causes span CWE-787 (Out-of-bounds Write), CWE-416 (Use After Free), and CWE-125 (Out-of-bounds Read), collectively enabling memory corruption within the browser engine. Exploitation requires network-based delivery (e.g., a malicious web page) and user interaction such as visiting a crafted site, but no special privileges are needed; attack complexity is rated High. The underlying bug list references over 100 individual Bugzilla entries, indicating a broad sweep of memory safety issues addressed in this release (Mozilla MFSA2026-30, Mozilla MFSA2026-32).

Impact

Successful exploitation of CVE-2026-6786 could allow a remote attacker to achieve arbitrary code execution on the affected system with high impact to confidentiality, integrity, and availability. Because Firefox and Thunderbird run on end-user workstations, exploitation could lead to full compromise of the user's session, credential theft, data exfiltration, or use of the system as a pivot point for lateral movement within a network. The vulnerability affects a wide user base given the broad deployment of Firefox and Thunderbird across enterprise and consumer environments (Mozilla MFSA2026-32, Mozilla MFSA2026-30).

Exploitability

As of the time of reporting, there is no public proof-of-concept exploit and no evidence of in-the-wild exploitation for CVE-2026-6786. The EPSS score is approximately 0.046%, reflecting a low current probability of exploitation. The vulnerability is not listed in the CISA Known Exploited Vulnerabilities (KEV) catalog. No threat actor attribution has been reported. The high attack complexity rating (requiring precise memory manipulation) reduces near-term weaponization risk, though the network-based delivery vector and broad user base make it a meaningful target over time (Mozilla MFSA2026-32).

Mitigation and workarounds

Mozilla has released patches addressing CVE-2026-6786 in Firefox 150, Firefox ESR 140.10, Thunderbird 150, and Thunderbird ESR 140.10, all announced on April 21, 2026. Organizations should immediately update all installations of Firefox and Thunderbird to these patched versions or later. No configuration-based workaround is available; upgrading is the only effective remediation. Downstream Linux distributions including Debian, Red Hat, Oracle Linux, Rocky Linux, AlmaLinux, SUSE, and Amazon Linux 2 have also released updated packages (Mozilla MFSA2026-30, Mozilla MFSA2026-32).

Community reactions

The CIS issued an advisory noting that multiple vulnerabilities in Mozilla products could allow for arbitrary code execution, recommending prompt patching (Mozilla MFSA2026-30). Mozilla published a blog post on hacks.mozilla.org titled "Behind the Scenes: Hardening Firefox" in May 2026, providing context on the fuzzing and security engineering efforts that uncovered this class of bugs. Community coverage noted that AI-assisted fuzzing (using Claude from Anthropic) contributed to finding several related memory safety issues in the same release cycle, generating discussion about the role of AI in vulnerability discovery.

Additional resources


SourceThis report was generated using AI

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