From mboxrd@z Thu Jan 1 00:00:00 1970 From: AL-KERNEL To: kernel-cve@kernelcve.org Subject: [CVE-2026-98952][MODERATE 7.0] pfcp: make sure the SEID is linear before reading it [ Upstream Date: Tue, 27 Oct 2026 01:04:02 -0400 Message-ID: MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit X-AL-KERNEL-CVE: CVE-2026-98952 X-AL-KERNEL-Priority: MODERATE 7.0 X-AL-KERNEL-Severity: MODERATE 7.0 X-AL-KERNEL-Base-Severity: MODERATE X-AL-KERNEL-KPANIC: YES X-AL-KERNEL-ActionableScore: 4 X-AL-KERNEL-ActionableScore-Lower: unknown X-AL-KERNEL-Commit: 3192a5e398c64d48aad64a09e29643e327369bc4 List-Id: CVE: CVE-2026-98952 Priority: MODERATE 7.0 AL-KERNEL base severity: MODERATE KPANIC flag: YES Patch: pfcp: make sure the SEID is linear before reading it [ Upstream Commit: 3192a5e398c64d48aad64a09e29643e327369bc4 Upstream patch: https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/commit/?id=3192a5e398c64d48aad64a09e29643e327369bc4 Original CVE announcement: https://lore.kernel.org/linux-cve-announce/?q=CVE-2026-98952 Analysis date: Tue, 27 Oct 2026 01:04:02 -0400 ActionableScore: 4 ActionableScore lower bound: unknown Actionable bucket: Actionable Moderate / Strong Important candidate requiring manual review Manual review required: YES Summary: A malformed remote PFCP packet can source an eight-byte SEID from beyond valid packet data, place it into tunnel metadata, and cause flower to classify using that corrupted identity. ====================================================================== ABOUT THIS REPORT ====================================================================== The original Linux kernel CVE announcement for CVE-2026-98952 is available here: https://lore.kernel.org/linux-cve-announce/?q=CVE-2026-98952 The original announcement does not normally provide a security severity estimate, CVSS assessment, or enough information to determine whether the reported kernel bug represents a practically relevant security issue. This report was generated by AL-KERNEL, an AI-assisted Linux kernel vulnerability analysis system developed by Alexander Larkin. It combines an autonomous classifier with LLM-assisted technical analysis and a separate ActionableScore mechanism. The purpose of this report is to prioritize Linux kernel CVEs before manual review, identify cases that require prompt investigation, and support automatic closure of issues that are unlikely to have meaningful security impact. Published priority for this report: MODERATE 7.0 Manual review required: YES A detailed explanation of the methodology and priority rules is included at the end of this message. ====================================================================== AL-KERNEL CLASSIFICATION RESULT ====================================================================== CVE-2026-98952 MODERATE https://www.kernelcve.org/list/?q=CVE-2026-98952 CHECK WITH IMPACT FROM ORIG NN LOW | AEGIS IMPACT MODERATE_KPANIC AEGIS_AS=1 LEGACY=MODERATE_KPANIC RESULT=SAME (final reconciliation agrees; DECISION: both MODERATE_KPANIC) Maybe valid. Check manually. Hints by AL-KERNEL: The best (paranoid) CVSS is 'AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L';CWE-125;*CWE-20;Other CVSS 'AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:N';BEST CVSS score: '7.3';DESCR 'pfcp_encap_recv validates only the UDP header and the four byte PFCP header, while pfcp_session_recv reads the following eight byte SEID when the S flag is set. A remote sender can provide a short or fragmented PFCP datagram so the read crosses valid linear packet data and stale bytes become tunnel metadata used by flower classification. For the CVSS the PR:N value is used because no authenticated session or local user context is required after a configured PFCP UDP endpoint becomes reachable. The demonstrated primitive is an out of bounds read with possible packet misclassification, not an out of bounds write, UAF, proven kernel data disclosure, or reliable kernel crash. Base AC:H reflects that a useful policy bypass requires suitable stale bytes and relevant flower rules. Automated impact decision path. The original SimpleNet classifier selected MODERATE_KPANIC. The primary LLM estimated paranoid CVSS 7.3 and the production ActionableScore returned 4 with recommendation Actionable Moderate / Strong Important candidate requiring manual review. The production rules then evaluated CVSS thresholds and ActionableScore thresholds. No severity changing production threshold or specialist override was recorded. The legacy KP specialist produced a review artifact and its decision was processed by the applicable KP rules. The legacy FD or DH downgrade specialist has no review artifact for this run. The independent weaker Aegis shadow evaluated CVSS unavailable and ActionableScore 1 with primitive class unknown and manual review unknown. Its independently reconciled impact was MODERATE_KPANIC. The shadow agreed with the final production impact and aggregation did not change that impact. The final reported impact is MODERATE_KPANIC.';YES REQUIRES MANUAL CHECK; ,and ActionableScore result is Actionable Moderate / Strong Important candidate requiring manual review (with actual score 4) MAYBE SIMPLEFIX NETWORK SKB LINUS KPANIC PACKET - - checked ====================================================================== ACTIONABLESCORE ANALYSIS ====================================================================== ActionableScore=4 PrimitiveClass=CLASS_B RealUAF=NO HostAdminRequired=NO NamespaceScopedPrivilege=UNKNOWN ImportantCandidate=YES ManualReview=REQUIRED AutoDowngradeAllowed=NO RequiresHostUID0=NO AuthorizationDecisionBypass=NO ProtectedResourceImpact=NONE B4PendingReview=NO B4PendingConfirmed=NO 1. ActionableScore * Raw base score: 4. * Conservative score: 4; no floor or cap applied. * Raw paranoid score: 4. * Paranoid score: 4; no additional effect is sufficiently proven. * Final recommended bucket: **Actionable Moderate / Strong Important candidate requiring manual review**:: 2. Signal breakdown * +2 Remote reachability: a network sender can submit a short or fragmented PFCP datagram to a configured PFCP UDP endpoint without an authenticated session. * +1 Weak/indirect corruption candidate: an eight-byte SEID is read beyond valid linear packet data and copied into structured tunnel metadata. * +1 Integrity plausible: the invalid SEID metadata is subsequently consumed by flower classification, potentially changing packet identity or classification. * No OOB-write, UAF, disclosure path, reliable crash, arbitrary write, controlled payload, or concrete policy bypass is shown. * B5-R does not apply: the defect is an OOB read, with no retry-accumulated OOB write. * B4-PENDING does not apply: no mandatory authorization guard changes from DENY to ALLOW. * The bounded metadata assignment is distinct from the invalid source read; it corrupts active metadata but is not itself an OOB kernel-memory write. 3. Reachability analysis The triggering actor is an unauthenticated remote sender once a PFCP endpoint is configured and reachable. Endpoint and flower-rule creation are prior control-plane setup, but the malformed-packet trigger itself does not require host-administrator powers or UID 0. The supplied evidence does not establish whether deployment occurs in the initial network namespace or a delegated namespace, so namespace-scoped privilege remains unknown. No race, retry sequence, special hardware, or additional vulnerability is required. 4. Primitive and severity reading The exact primitive is an OOB packet-data read whose stale result is written into active tunnel metadata and later trusted by the privileged flower classifier. This is CLASS_B: the initial access is read-only, but the resulting corrupted structured state escapes into active policy processing. Shown effects are incorrect SEID metadata and downstream classification on that metadata. Actual firewall bypass, cross-namespace interference, disclosure, host crash, or privilege escalation remain unproven. ImportantCandidate is retained because there is a specific external-input → corrupted active metadata → privileged policy-consumer chain with unresolved trust-boundary consequences. Auto-downgrade is disallowed pending examination of real flower rules and namespace/host consumers. 5. One-sentence report phrase A malformed remote PFCP packet can source an eight-byte SEID from beyond valid packet data, place it into tunnel metadata, and cause flower to classify using that corrupted identity. 6. Manual review advice MANUAL CHECK REQUIRED Review downstream flower actions and namespace or host policy boundaries; the non-crash metadata-integrity chain is concrete, while its final protected-policy impact is unresolved. 7. Call-site confidence Call-site confidence: medium ====================================================================== UPSTREAM PATCH SUMMARY ====================================================================== Patch: pfcp: make sure the SEID is linear before reading it [ Upstream Commit: 3192a5e398c64d48aad64a09e29643e327369bc4 Upstream URL: https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/commit/?id=3192a5e398c64d48aad64a09e29643e327369bc4 Changed files: drivers/net/pfcp.c Diff excerpt: Not included in this email. See the upstream URL for the full patch. Full patch: https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/commit/?id=3192a5e398c64d48aad64a09e29643e327369bc4 ====================================================================== DETAILED REPORT METHODOLOGY ====================================================================== The original Linux kernel CVE announcement for CVE-2026-98952 can be found here: https://lore.kernel.org/linux-cve-announce/?q=CVE-2026-98952 The original CVE announcement normally does not include a security-level estimate. In particular, it may not contain a CVSS assessment, an impact level, or enough information to determine whether the reported bug is a practically relevant security issue. One purpose of this parallel CVE list is to provide that missing technical and prioritization information. The original goal of the AL-KERNEL project was to prioritize Linux kernel CVE analysis automatically before manual review. The system can also help identify non-security issues that may be suitable for automatic closure. This report was generated by AL-KERNEL, an AI-assisted Linux kernel vulnerability analysis system developed by Alexander Larkin. The first analysis stage combines an autonomous classifier with additional LLM-based analysis. The autonomous classifier runs locally on a CPU and is based on a backpropagation neural network. Together, these mechanisms produce a technical vulnerability description, identify likely weakness types, estimate CVSS severity, and provide input for ActionableScore. Two CVSS estimates are retained because incomplete kernel vulnerability information often permits more than one defensible interpretation: Conservative CVSS vector: AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:N The Best / paranoid CVSS vector: AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L The Best / paranoid CVSS score: 7.3 The conservative vector represents a lower-impact interpretation. The Best/paranoid vector intentionally represents a plausible upper-bound interpretation and should not automatically be treated as demonstrated real-world impact. CVSS may also need to be adjusted for a particular Linux deployment, because actual reachability, privileges, enabled kernel configuration, hardware, namespaces, exposed device nodes, and other environmental conditions can differ significantly between systems. A separate ActionableScore mechanism evaluates practical remediation urgency. Its analysis may include reachability, attack prerequisites, subsystem exposure, memory-corruption characteristics, denial-of-service reliability, and possible confidentiality, integrity, or privilege-escalation impact. Conservative ActionableScore: unknown Paranoid ActionableScore: 4 The final base severity is taken directly from the second tab-separated field of the AL-KERNEL classification result. ActionableScore does not replace or independently override that final AL-KERNEL decision, and if ActionableScore adjusted impact level of ALKERNEL, then you would see self-readable flags above like INCREASED_TO_HIGH_BASED_ON_ACTIONABLESCOREHIGHEREQTHAN7. For an AL-KERNEL result of MODERATE, this report uses the following additional presentation split: ActionableScore below 5 -> MODERATE REGULAR ActionableScore 5 or more -> MODERATE 7.0 The distinction between MODERATE REGULAR and MODERATE 7.0 makes it possible to identify Moderate issues that should receive manual analysis and fixes before lower-priority MODERATE REGULAR issues. In many cases, MODERATE REGULAR fixes may wait for a later rebase or routine update. There is one override in which MODERATE REGULAR becomes MODERATE 7.0 even when the ActionableScore is below 5. When the AL-KERNEL result contains the KPANIC flag, a MODERATE result is always presented as MODERATE 7.0. The KPANIC flag selected with few regexps without usage of AI at all, so it helps to detect cases when Kernel Crash happens and similar (to filter False-Negative results from the LLM usage). KPANIC indicates that a reliable kernel crash, kernel panic, or similarly serious kernel availability impact was identified by the classification workflow. AL-KERNEL base severity for this report: MODERATE KPANIC detected for this report: YES Published priority for this report (same as in Subject): MODERATE 7.0 These results are intended to support engineering triage. They are machine-generated estimates, and cases marked for manual review should be validated by a human security engineer before final disposition. For more info read docs linked from here: https://kernelcve.org/ (and you can submit you own patch there to generate such a report for non-existant CVE-id yet). Note that in many cases this AI tool selects higher severity, than real is (means you can expect Importants instead of Moderate 7.0 or Moderates 7.0 instead of regular Moderates). If you see such cases, please use reply email interface to add additional manual analyses info to this particular CVE. And please, please, let me know when you see Lows instead of Importants or Important instead of Low (because particular for such cases I need to tune this AI tool to make it better for this one and next similar). My contact email for such notifications is alexanjelausa@gmail.com (and both send reply to CVE record itself too and see "reply" button below for howto reply).