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Sound the depths of every vulnerability.

Newly released CVEs across every platform — sleek to read, verbose on demand, and served raw as markdown for AI and agent ingestion. Severity reads as depth: the deeper the contact, the graver the threat.

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Depth = severity + exploitation

HadalCritical and actively exploited (CISA KEV / 0day)
AbyssalCritical with a public exploit or in-the-wild use
MidnightCritical, or high with PoC / in-the-wild
TwilightHigh severity, or a signal on a lesser flaw
SunlitLow / medium · no exploitation signal

70220

Tracked CVEs

6543

Critical

746

Exploited in wild

3743

Last 7 days

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Bluesky + Hacker News mentions, last 7 days

1CVE-2026-109618Critical· 9.2RCE via OOM corruption4896
2CVE-2026-109619Critical· 9.2RCE via stale global state2196
3CVE-2026-111497Critical· 9.2Out-of-Bounds Write in Google Brotli Decoder1892
4CVE-2026-99999Critical· 9.3In the Linux kernel, the following vulnerability has been resolved: ipv4: validate checksum_start before completing checksum If a packet with bad checksum metadata gets into the ipv4 stack, skb_checksum_help can corrupt the network hea…963
5CVE-2026-110264Critical· 10.0Integer Overflow or Wraparound in GitLab367
6CVE-2026-91403Critical· 9.1Vulnerability in the Oracle Java SE, Oracle GraalVM for JDK, Oracle GraalVM Enterprise Edition product of Oracle Java SE (component: JSSE)262

This week's digest →All digests

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11 matches

CVE-2026-118684Medium· 5.9

6d ago

MONAI before 1.6.1 Missing Integrity Verification via check_hash in Downloads

MONAI before 1.6.1 contains an insufficient integrity verification vulnerability in check_hash() that silently returns True when hash_val is None, with MD5 as the default hash type. Attackers performing man-in-the-middle or registry comp…

▾ SunlitProject-MONAI · MONAIEPSS 0.18%via CVEORG

CVE-2026-118683High· 8.8

6d ago

MONAI 1.2.0 before 1.6.1 Code Execution via bundle configs/logging.conf

MONAI from 1.2.0 before 1.6.1 contains a remote code execution vulnerability in ConfigWorkflow.__init__, which passes a bundle's configs/logging.conf to logging.config.fileConfig that eval()s handler class and args fields. Attackers can …

▾ TwilightProject-MONAI · MONAIEPSS 0.49%via CVEORG

CVE-2026-109361High· 7.3

3w ago

MONAI 1.2.0 before 1.6.1 contains an insecure deserialization vulnerability in nnUNetV2Runner that unpickles the postprocessing_file path read from inference_information.json without confining it to the results directory

MONAI 1.2.0 before 1.6.1 contains an insecure deserialization vulnerability in nnUNetV2Runner that unpickles the postprocessing_file path read from inference_information.json without confining it to the results directory. Attackers who c…

▾ TwilightProject-MONAI · MONAIEPSS 0.23%via NVD

CVE-2026-109360High· 7.5PoC

3w ago

MONAI 0.4.0 before 1.6.1 contains a remote code execution vulnerability that allows on-path attackers to execute code by tampering with SENet pretrained weights fetched over plaintext HTTP

MONAI 0.4.0 before 1.6.1 contains a remote code execution vulnerability that allows on-path attackers to execute code by tampering with SENet pretrained weights fetched over plaintext HTTP. Attackers can substitute a crafted .pth file th…

▾ MidnightProject-MONAI · MONAIEPSS 0.56%via NVD

CVE-2026-100846High· 7.6PoC

1mo ago

MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py

MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py. The function reads a .pkl file and passes its contents to pickle.loads without validating the dat…

▾ Midnightproject-monai · monaiEPSS 0.36%via NVD

CVE-2026-100845High· 7.8PoC

1mo ago

MONAI before 1.6.0 contains an unsafe deserialization vulnerability in the NumpyReader class that unconditionally uses numpy.load with allow_pickle=True when loading .npy and .npz files

MONAI before 1.6.0 contains an unsafe deserialization vulnerability in the NumpyReader class that unconditionally uses numpy.load with allow_pickle=True when loading .npy and .npz files. Attackers can craft malicious .npy files with pick…

▾ Midnightproject-monai · monaiEPSS 0.20%via NVD

CVE-2026-100844High· 8.4PoC

1mo ago

MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component (monai.apps.nnunet.nnunetv2_runner)

MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component (monai.apps.nnunet.nnunetv2_runner). User-controlled values taken from the YAML configuration file (notably dataset_name_or_id) and from CLI/kwargs …

▾ Midnightproject-monai · monaiEPSS 0.92%via NVD

CVE-2026-100843High· 7.8PoC

1mo ago

MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py

MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py. Attackers can craft malicious pickle files that execut…

▾ Midnightproject-monai · monaiEPSS 0.30%via NVD

CVE-2026-100842High· 7.0PoC

1mo ago

MONAI through 1.6.0 contains an eval injection vulnerability in _get_fake_spatial_shape() in monai/bundle/scripts.py

MONAI through 1.6.0 contains an eval injection vulnerability in _get_fake_spatial_shape() in monai/bundle/scripts.py. The function validates shape expressions with a helper that walks the AST and only collects ast.Name nodes, rejecting a…

▾ Midnightproject-monai · monaiEPSS 0.18%via NVD

CVE-2026-100841High· 7.8

1mo ago

In MONAI 1.6.0, PersistentDataset (monai/data/dataset.py) explicitly rejects the combination track_meta=True with weights_only=True, forcing users who cache MetaTensors (the default tensor type in MONAI >= 1.0) to run torch.load(hashfile…

In MONAI 1.6.0, PersistentDataset (monai/data/dataset.py) explicitly rejects the combination track_meta=True with weights_only=True, forcing users who cache MetaTensors (the default tensor type in MONAI >= 1.0) to run torch.load(hashfile…

▾ Twilightproject-monai · monaiEPSS 0.13%via NVD

CVE-2026-100840High· 7.8PoC

1mo ago

MONAI through 1.6.0 contains a remote code execution vulnerability in the bundle configuration engine that resolves _target_ values to arbitrary importable callables without an allow list and passes $ expressions to Python eval()

MONAI through 1.6.0 contains a remote code execution vulnerability in the bundle configuration engine that resolves _target_ values to arbitrary importable callables without an allow list and passes $ expressions to Python eval(). Attack…

▾ Midnightproject-monai · monaiEPSS 0.31%via NVD

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Oct 27, 2026

Sound the depths of every vulnerability.

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MONAI before 1.6.1 Missing Integrity Verification via check_hash in Downloads

MONAI 1.2.0 before 1.6.1 Code Execution via bundle configs/logging.conf

MONAI 1.2.0 before 1.6.1 contains an insecure deserialization vulnerability in nnUNetV2Runner that unpickles the postprocessing_file path read from inference_information.json without confining it to the results directory

MONAI 0.4.0 before 1.6.1 contains a remote code execution vulnerability that allows on-path attackers to execute code by tampering with SENet pretrained weights fetched over plaintext HTTP

MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py

MONAI before 1.6.0 contains an unsafe deserialization vulnerability in the NumpyReader class that unconditionally uses numpy.load with allow_pickle=True when loading .npy and .npz files

MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component (monai.apps.nnunet.nnunetv2_runner)

MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py

MONAI through 1.6.0 contains an eval injection vulnerability in _get_fake_spatial_shape() in monai/bundle/scripts.py

In MONAI 1.6.0, PersistentDataset (monai/data/dataset.py) explicitly rejects the combination track_meta=True with weights_only=True, forcing users who cache MetaTensors (the default tensor type in MONAI >= 1.0) to run torch.load(hashfile…

MONAI through 1.6.0 contains a remote code execution vulnerability in the bundle configuration engine that resolves _target_ values to arbitrary importable callables without an allow list and passes $ expressions to Python eval()

From the blog

VulnSea has moved to vulnsea.com (and what changed since September)

What's new: open corpus, three new MCP tools, vendor advisories, package pages

Wiring VulnSea into an agent: a hands-on tutorial

Sound the depths of every vulnerability.

Trending now

Feed

MONAI before 1.6.1 Missing Integrity Verification via check_hash in Downloads

MONAI 1.2.0 before 1.6.1 Code Execution via bundle configs/logging.conf

MONAI 1.2.0 before 1.6.1 contains an insecure deserialization vulnerability in nnUNetV2Runner that unpickles the postprocessing_file path read from inference_information.json without confining it to the results directory

MONAI 0.4.0 before 1.6.1 contains a remote code execution vulnerability that allows on-path attackers to execute code by tampering with SENet pretrained weights fetched over plaintext HTTP

MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py

MONAI before 1.6.0 contains an unsafe deserialization vulnerability in the NumpyReader class that unconditionally uses numpy.load with allow_pickle=True when loading .npy and .npz files

MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component (monai.apps.nnunet.nnunetv2_runner)

MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py

MONAI through 1.6.0 contains an eval injection vulnerability in _get_fake_spatial_shape() in monai/bundle/scripts.py

In MONAI 1.6.0, PersistentDataset (monai/data/dataset.py) explicitly rejects the combination track_meta=True with weights_only=True, forcing users who cache MetaTensors (the default tensor type in MONAI >= 1.0) to run torch.load(hashfile…

MONAI through 1.6.0 contains a remote code execution vulnerability in the bundle configuration engine that resolves _target_ values to arbitrary importable callables without an allow list and passes $ expressions to Python eval()

From the blog

VulnSea has moved to vulnsea.com (and what changed since September)

What's new: open corpus, three new MCP tools, vendor advisories, package pages

Wiring VulnSea into an agent: a hands-on tutorial