[CVE-2026-98851][LOW] netfilter: nft_flow_offload: drop flowtable reference on init error path

[AL-KERNEL <alkernel@kernelcve.org>]

CVE: CVE-2026-98851
Priority: LOW
AL-KERNEL base severity: LOW
KPANIC flag: NO
Patch: netfilter: nft_flow_offload: drop flowtable reference on init error path
Commit: 64ac0cb574c740bf723affb79b646eeba920db6b
Upstream patch: https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/commit/?id=64ac0cb574c740bf723affb79b646eeba920db6b
Original CVE announcement: https://lore.kernel.org/linux-cve-announce/?q=CVE-2026-98851
Analysis date: Sat, 24 Oct 2026 04:55:40 -0400
ActionableScore: 1
ActionableScore lower bound: unknown
Actionable bucket: Ordinary Moderate
Manual review required: NO

Summary:
A failed nft flow-offload expression initialization leaks one flowtable use reference, leaving the flowtable undeletable without demonstrating memory corruption or host-wide denial of service.

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ABOUT THIS REPORT
======================================================================

The original Linux kernel CVE announcement for CVE-2026-98851 is available here:
https://lore.kernel.org/linux-cve-announce/?q=CVE-2026-98851

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: LOW
Manual review required: NO

A detailed explanation of the methodology and priority rules is included
at the end of this message.

======================================================================
AL-KERNEL CLASSIFICATION RESULT
======================================================================

CVE-2026-98851	LOW	https://www.kernelcve.org/list/?q=CVE-2026-98851 CHECK WITH IMPACT FROM ORIG NN LOW | AEGIS IMPACT LOW AEGIS_AS=1 LEGACY=LOW RESULT=SAME (final reconciliation agrees; DECISION: both LOW)	Maybe valid. Check manually. Hints by AL-KERNEL: The best (paranoid) CVSS is 'AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L';CWE-911;*CWE-772;Other CVSS 'AV:L/AC:H/PR:H/UI:N/S:U/C:N/I:N/A:L';BEST CVSS score: '2.5';DESCR 'A failed nft_flow_offload expression initialization can leave the flowtable use count permanently elevated because nft_use_inc succeeds before nf_ct_netns_get fails. Upper layer cleanup does not call the failed expression destroy callback, so each affected rule addition leaves a stale logical reference and NFT_MSG_DELFLOWTABLE continues to return EBUSY. For the CVSS the PR:H value reflects that reliable triggering normally requires CAP_NET_ADMIN and control over nftables rule creation. The paranoid PR:L alternative covers delegated network administration or a container service account, but the demonstrated impact remains limited to flowtable lifecycle management and there is no memory corruption or host wide resource exhaustion primitive. Automated impact decision path. The original SimpleNet classifier selected LOW. The primary LLM estimated paranoid CVSS 2.5 and the production ActionableScore returned 1 with recommendation Ordinary Moderate. The production rules then evaluated CVSS thresholds and ActionableScore thresholds. No severity changing production threshold or specialist override was recorded. The legacy KP specialist has no review artifact for this run. The legacy FD or DH downgrade specialist has no review artifact for this run. The independent weaker Aegis shadow evaluated CVSS 1.9 and ActionableScore 1 with primitive class CLASS_C and manual review REQUIRED. Its independently reconciled impact was LOW. The shadow agreed with the final production impact and aggregation did not change that impact. The final reported impact is LOW.';NO MANUAL CHECK; ,and ActionableScore result is Ordinary Moderate (with actual score 1)	MAYBE	REMOTE SIMPLEFIX NETFILTER LEAK ERRORPATH LINUS 	-	-	checked

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ACTIONABLESCORE ANALYSIS
======================================================================

ActionableScore=1
PrimitiveClass=CLASS_C
RealUAF=NO
HostAdminRequired=UNKNOWN
NamespaceScopedPrivilege=UNKNOWN
ImportantCandidate=NO
ManualReview=NO
AutoDowngradeAllowed=YES
RequiresHostUID0=UNKNOWN
AuthorizationDecisionBypass=NO
ProtectedResourceImpact=NONE
B4PendingReview=NO
B4PendingConfirmed=NO

1. ActionableScore

* Raw base score: 1.
* Conservative score: 1.
* Raw paranoid score: 1.
* Paranoid score: 1.
* No floor or cap applied.
* Final recommended bucket: **Ordinary Moderate**::

2. Signal breakdown

* +1 — Concrete persistent kernel reference leak: `nft_use_inc()` increments `flowtable->use`, but failure from `nf_ct_netns_get()` previously returned without the matching `nft_use_dec()`. Upper-layer cleanup skips the failed expression, so the reference remains permanently elevated.
* +0 — The resulting flowtable cannot be deleted because `NFT_MSG_DELFLOWTABLE` returns `-EBUSY`; this is the direct consequence of the same leak and is not counted again as availability impact.
* No UAF, OOB access, repeated release, memory corruption, authorization bypass, host crash, cross-boundary corruption, or broad resource exhaustion is shown.
* No B5-R retry-accumulated OOB write or B3-PENDING shared-backing write exists.
* No additional privilege penalty: CAP_NET_ADMIN is indicated, but the evidence does not establish that it must be host/init-namespace administration rather than delegated or namespace-scoped authority.

3. Reachability analysis

The trigger requires nftables rule creation and a failing `nf_ct_netns_get()` during flow-offload expression initialization. CAP_NET_ADMIN is normally associated with rule creation, but the supplied evidence does not identify the governing user namespace or prove that the complete trigger works in an attacker-created namespace. It also does not establish host UID 0 as mandatory. No special hardware or timing race is involved; however, reliable attacker control over the failure condition is not fully demonstrated.

4. Primitive and severity reading

The exact primitive is a persistent logical flowtable reference leak producing undeletable flowtable state. It is passive lifecycle/bookkeeping corruption, not a stale-object access or post-lifetime mutation. The shown effect is restricted flowtable lifecycle management (`-EBUSY` on deletion). Refcount overflow, kernel memory corruption, host-wide exhaustion, and privilege escalation are unproven.

This is CLASS_C invalid-state/resource-lifecycle behavior. ImportantCandidate is NO because no material corruption or security-boundary primitive remains unresolved. Auto-downgrade is allowed.

5. One-sentence report phrase

A failed nft flow-offload expression initialization leaks one flowtable use reference, leaving the flowtable undeletable without demonstrating memory corruption or host-wide denial of service.

6. Manual review advice

NO MANUAL CHECK NEEDED

The patch and cleanup behavior clearly establish a constrained reference leak with no Important-preserving primitive.

7. Call-site confidence

Call-site confidence: high

======================================================================
UPSTREAM PATCH SUMMARY
======================================================================

Patch: netfilter: nft_flow_offload: drop flowtable reference on init error path
Commit: 64ac0cb574c740bf723affb79b646eeba920db6b
Upstream URL: https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/commit/?id=64ac0cb574c740bf723affb79b646eeba920db6b

Commit description:

nft_flow_offload_init() bumps the flowtable use count with
nft_use_inc() before calling nf_ct_netns_get().  When the latter
fails, the error is returned as-is and the reference is leaked.

The upper layers do not balance it either: nf_tables_newexpr()
clears expr->ops when the expression init callback fails, so the
nft_expr_more() iteration in nft_rule_expr_deactivate() and
nf_tables_rule_destroy() stops right before the failed expression
and its ->destroy callback, which would drop the reference, never
runs.

Each failed rule addition therefore leaks one flowtable reference
and the flowtable can no longer be removed: NFT_MSG_DELFLOWTABLE
keeps reporting -EBUSY even though no rule references it.

Save the nf_ct_netns_get() return value and undo the nft_use_inc()
when it fails, restoring the inc/dec pairing within
nft_flow_offload_init() itself.

Fixes: a3c90f7 ("netfilter: nf_tables: flow offload expression")
Reported-by: TencentOS Corvus AI <corvus@tencent.com>
Cc: stable@vger.kernel.org
Assisted-by: CodeBuddy:Kimi-K3
Signed-off-by: Aohan Mei <henrymei@tencent.com>
Signed-off-by: Pablo Neira Ayuso <pablo@netfilter.org>
Signed-off-by: Greg Kroah-Hartman <gregkh@linuxfoundation.org>

Changed files:
  net/netfilter/nft_flow_offload.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=64ac0cb574c740bf723affb79b646eeba920db6b

======================================================================
DETAILED REPORT METHODOLOGY
======================================================================

The original Linux kernel CVE announcement for CVE-2026-98851 can be found here:
https://lore.kernel.org/linux-cve-announce/?q=CVE-2026-98851

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:L/AC:H/PR:H/UI:N/S:U/C:N/I:N/A:L
  The Best / paranoid CVSS vector: AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
  The Best / paranoid CVSS score: 2.5

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: 1

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: LOW
KPANIC detected for this report: NO
Published priority for this report (same as in Subject): LOW

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).