FastNetMon is a high-performance DDoS detector and sensor. In version 1.2.9 and earlier, peer_find_template() in src/netflow_plugin/netflow_collector.cpp and src/netflow_plugin/netflow_v9_collector.cpp returns a pointer into global_netflow9_templates after releasing the cache mutex. On deployments with at least two entries in netflow_ports, concurrent template redefinition and data-flowset processing can let add_update_peer_template() reassign the template and free its records vector while another listener dereferences the escaped pointer. An unauthenticated sender that can reach both NetFlow/IPFIX listener ports can trigger a heap-use-after-free and reliably crash FastNetMon. No fixed release is available as of this review.

FastNetMon is a high-performance DDoS detector and sensor. In version 1.2.9 and earlier, process_sflow_flow_sample() in src/sflow_plugin/sflow_collector.cpp passes the attacker-controlled sflow_raw_protocol_header_t.header_size to parse_raw_packet_to_simple_packet_full() as its trusted captured length without comparing it with the bytes remaining in the record. An inflated header_size creates a false end pointer beyond the received sFlow v5 UDP buffer, defeating the nested parser's bounds checks and causing an unauthenticated remote out-of-bounds read and process crash, with possible limited exposure of adjacent bytes through parsed flow fields. No fixed release is available as of this review.

FastNetMon is a high-performance DDoS detector and sensor. In version 1.2.9 and earlier, the NetFlow v9 collector calls  strlen()  on an attacker-controlled interface-description field without bounding the scan to the field’s declared length. Although fields longer than 128 bytes are excluded from this operation, an accepted field without a null terminator can cause reads beyond the field and packet buffer. An unauthenticated remote attacker who can send crafted UDP packets to the collector can trigger an out-of-bounds read and potentially crash the process, causing denial of service. No fixed release is available as of this review.

FastNetMon is a high-performance DDoS detector and sensor. In version 1.2.9 and earlier, the live parse_packet() callback in src/pcap_plugin/pcap_collector.cpp is registered through pcap_loop and processes captured frames when pcap = on without checking packethdr->caplen. A short frame or an attacker-controlled IPv4 header length can make the parser dereference IP or transport headers beyond the captured bytes, causing an out-of-bounds read and process termination. No fixed release is available as of this review.

FastNetMon is a high-performance DDoS detector and sensor. In version 1.2.9 and earlier, FastNetMon's IPv6 top-talkers screen-draw thread in draw_table_ipv6() in src/fastnetmon_logic.cpp iterates ipv6_host_counters.average_speed_map without holding counter_map_mutex while recalculate_speed() inserts new keys and may rehash the same map. The race requires networks_list to include the targeted IPv6 range. It causes confirmed undefined behavior under load; a process crash is plausible but was not observed, so reliable denial of service has not been established. No fixed release is available as of this review.

FastNetMon is a high-performance DDoS detector and sensor. In version 1.2.9 and earlier, parse_raw_packet_to_simple_packet_full() in src/simple_packet_parser_ng.cpp dereferences ipv6_extension_header_fragment_t when IpProtocolNumberIPV6_FRAG is present without first verifying that the eight-byte Fragment header remains. A crafted sFlow v5 RAW_PACKET_HEADER record can remotely reach this shared parser with the Fragment bytes omitted, causing an unauthenticated remote out-of-bounds read. The supplied harness detects the read under AddressSanitizer, but the advisory does not establish a production process crash. No fixed release is available as of this review.

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.16.1, the multi-user API server treats the empty namespace used by shared or global pipelines as an authorization exception for modification and deletion operations. An authenticated user can modify or delete shared pipeline definitions and versions without the required Kubernetes RBAC permissions, compromising their integrity and availability. Read-only access to shared pipelines remains intentional, single-user deployments intentionally bypass multi-user checks, and execution of a modified pipeline is a conditional downstream consequence. The separate shared-pipeline multipart upload authorization path is outside this advisory and is not remediated by version 2.16.1. This issue is fixed in version 2.16.1.

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the legacy V1/raw-Argo execution cache mutating webhook and cache-server storage path hash selected template fields without the Kubernetes namespace, use the key alone for lookups and duplicate checks, and trust the mutable pipelines.kubeflow.org/execution_cache_key annotation when publishing outputs. In multi-user deployments where tenant namespaces share the cache service, a tenant with workload-submission privileges can read another namespace's cached outputs or seed matching outputs that are substituted into a later step. Standard KFP V2 component pods bypass this cache, matching requires the selected template fields and staleness policy, and arbitrary code execution or service availability impact is not established. This issue is fixed in version 2.18.0.

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the frontend artifact download path for source=s3 or source=minio accepts providerInfo.Params.endpoint, and the archived pod-log path accepts S3-compatible endpoint metadata from workflow status, without requiring those endpoints to match operator-controlled storage configuration. In the shipped authorization-disabled standalone configuration, an unauthenticated caller can make the shared ml-pipeline-ui pod connect to arbitrary reachable hosts and return response data; requests may also reveal an access-key identifier when signed with configured object-store credentials. Multi-user secret-backed metadata remains restricted to the server namespace. This issue is fixed in version 2.18.0.

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the frontend GET /artifacts/get route accepts source=http or source=https with caller-controlled bucket and key values, and isAllowedResourceName permits an all-digit decimal host that the URL parser converts to an IPv4 address. The default ALLOWED_ARTIFACT_DOMAIN_REGEX and authorization-disabled standalone configuration allow an unauthenticated caller to fetch internal HTTP resources and receive their response body. In the authenticated multi-user configuration, the same destination-validation defect is reachable by a low-privilege tenant with a valid identity and authorized namespace. Cloud credential disclosure depends on reachable metadata services and attached roles, and the issue does not establish IMDSv2 bypass or credential access when metadata is blocked. This issue is fixed in version 2.18.0.