CVE-2026-61762 Threat Intelligence Report

Executive Summary

CVE-2026-61762 is an unauthenticated (local-privilege) deserialization-of-untrusted-data vulnerability in Nvidia's NeMo Megatron Bridge component. An attacker with local workstation access can trigger deserialization of attacker-controlled data, leading to arbitrary code execution, data tampering, and information disclosure with high confidentiality, integrity, and availability impact (CVSS 3.1 base score 7.8). The vulnerability is presently unpatched; public exploit proof-of-concept material exists, and no vendor advisory or fix has been issued as of this reporting date.

Technical Analysis

Vulnerability Class. CVE-2026-61762 is cataloged under CWE-502 — Deserialization of Untrusted Data. When an application deserializes data from an attacker-controlled source without integrity verification or allow-listing of classes/types, an attacker can supply a crafted object graph that, when instantiated during deserialization, triggers attacker-controlled logic — commonly referred to as a "gadget chain." In .NET, Java, and Python ecosystems (each relevant to the NeMo Megatron Bridge since it involves model serialization formats), insecure deserialization often leads directly to remote/local code execution, file overwrite (data tampering), or memory disclosure.

Attack Surface. The NVD description specifically references "Nvidia NeMo Megatron Bridge" — a component that facilitates bridging of Megatron-LM (Megatron-Core) model checkpoints and weights into the NeMo framework. NeMo Megatron Bridge handles serialized model artifacts — specifically, checkpoint files, tokenizer merges, and configuration payloads. An attacker who can deliver or substitute a maliciously crafted serialized object — e.g., a poisoned model checkpoint saved in a .ckpt, .safetensors, or Python pickle-equivalent format — can achieve code execution at the privilege level of the process performing deserialization.

Trigger Mechanisms. Because the flaw is triggered by deserialization of attacker-controlled data, a realistic carrier medium is any data ingestion pathway into the NeMo Megatron Bridge: loading a user-supplied model or checkpoint file, ingesting a training run artifact, or importing configuration blobs from model registries. The NVD description and CNA title provide insufficient technical detail to confirm the specific serialization library (e.g., pickle, torch.load, Java ObjectInputStream, or .NET BinaryFormatter) or the precise object class exploited. Based on the vendor, Pythonic tooling is most probable — torch.load() and PyTorch/NeMo's handling of .ckpt files frequently rely on pickle, a well-documented source of insecure-deserialization vulnerabilities.

Code-level specifics (exact function names, class names, call sites) have not been disclosed within the NVD description. When the MITRE CNA description is sparse, analysts must treat the vulnerability as class-level (CWE-502) rather than assume concrete implementation details.

Patch Status. The Patch Confidence score of 0/4, an empty patch-source list, and a Vendor Fix: N/A label confirm that no official patch is available. The vendor has not communicated a remediation timeline at the time of this writing. Affected version enumeration is not yet published.

Exploitation Scenarios

Attack chain analysis for CVE-2026-61762 is derived from the vulnerability class and the NVD description. No public exploit repository or technical write-up with a step-by-step reproduction has been released as of the date of this report; however, a public PoC announcement exists.

Scenario 1 — Malicious Model Checkpoint Loading (Primary)

1. Reconnaissance / Target Identification. An attacker with local access or a foothold on a workstation or server that runs NeMo/Megatron Bridge identifies the usage of model-checkpoint ingestion. This can be accomplished by inspecting running processes (nvidia-smi, ps listing for python/PyTorch processes), examining application logs, or checking for directories containing .ckpt/Megatron artifacts that the victim will load.

2. Triggering the Flaw. The attacker crafts a malicious serialized payload — a model checkpoint file or equivalent serialized artifact — such that it contains an embedded gadget chain that executes on unpickling/unmarshalling. The attacker plants the file in a location the victim application reads from — local storage, shared network mount, or artifact registry — or convinces the victim to load an untrusted checkpoint.

3. Payload / Execution. Upon the victim application invoking deserialization of the attacker-controlled data (loading the poisoned checkpoint), the gadget chain executes arbitrary code at the privilege level of the NeMo/Megatron Bridge Python process. In typical enterprise ML environments, the model training/inference service runs as a dedicated user but may have access to GPUs, dataset stores, model registry credentials, and internal MLOps APIs.

4. Post-Exploitation Impact. Code execution can lead to data tampering (modifying model weights or training set labels), information disclosure (reading proprietary model weights, API keys, dataset files), or lateral movement within the ML infrastructure. Because the CVSS vector indicates S:U (scope unchanged) and PR:L (low privileges required), the attacker needs only low-level local privileges to mount the attack on the underlying OS account.

Scenario 2 — Shared Model Registry / Artifact Poisoning

In environments where NeMo Megatron Bridge retrieves model artifacts from a central MLOps artifact store or shared container registry, an attacker who can push/modify one serialized model object could achieve code execution on every analyst/engineer workstation that pulls and deserializes that artifact. Public PoC availability raises the likelihood of adoption into automated exploit tooling once vendor patch status changes (or, conversely, speeds up exploitation while unpatched).

Note: No public PoC or exploit code is available as separate artifacts (Nuclei template: NO, exploit code: NO); therefore, specifics of the attack chain are inferred from the vulnerability class and threat modeling, not from observed operational exploitation.

Impact Assessment

CVSS Decomposition. The CVSS vector CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H yields a base score of 7.8 (High):

• Attack Vector: Local (AV:L). Exploitation requires local access to the target system, but "local" covers any file the affected user process reads, including files pulled from network shares.
• Attack Complexity: Low (AC:L). No conditions outside the attacker's control are required; standard deserialization gadget chains for the ecosystem suffice.
• Privileges Required: Low (PR:L). A low-privileged user (non-admin) can trigger the vulnerable path.
• User Interaction: None (UI:N). No victim interaction required beyond the system's normal operation of loading model assets.
• Scope: Unchanged (S:U). The compromise is constrained to the security scope of the vulnerable component.
• CIA Impact: High/High/High. Full compromise of confidentiality, integrity, and availability within the affected process context.

Business Impact. For organizations in the AI/ML software lifecycle, this vulnerability is significant. Enterprise assets at risk:

• Training/inference pipelines that use NeMo Megatron Bridge to import third-party or community-provided checkpoints/weights.
• MLOps platform servers performing model deserialization.
• Data science workstations of employees that "pull" models from shared registries.

Blast Radius. Confidentiality loss could leak proprietary model weights — a considerable intellectual property issue. Integrity loss could silently alter trained models (data poisoning is a viable tampering outcome). Availability impact can result in process crashes or persistent denial-of-service. Because Python pickle/PyTorch loading produces code execution in the process context, the meaningful vertical-privilege boundary is limited by OS user isolation, not the application.

Exploitability in the Wild. EPSS has not yet scored this CVE (FIRST.org). No evidence of active exploitation in the wild has been publicly documented. CISA KEV status: NO. Public PoC material is confirmed available.

Detection Guidance

Detecting untrusted-deserialization attempts requires a combination of file-level telemetry (e.g., process execution parents, anomalies in the process chain that loads files), network indicators (where applicable), and endpoint anomaly detection focused on unusual subprocess or command execution from Python processes. Provided below are detection rules tailored for this class of attack.

Sigma Rule

title: Suspicious Deserialization Behavior in Nvidia NeMo / Megatron Bridge Processes
id: e8f4c17a-3b61-4a01-9d26-9b1f4a8d06c2
status: experimental
description: Detects anomalies consistent with CWE-502 deserialization exploitation in NeMo/Megatron Bridge. Focuses on processes named for Python/PyTorch model loaders spawning child processes or executing unusual commands shortly after file-open events on model artifacts.
references:
    - https://nvd.nist.gov/vuln/detail/CVE-2026-61762
author: Valtersit Threat Intelligence
date: 2026/06/01
logsource:
    category: process_creation
    product: linux
detection:
    selection_parent_process:
        ParentImage|endswith:
            - '/python3'
            - '/python'
            - '/torch'
        ParentCommandLine|contains:
            - 'megatron'
            - 'nemo'
            - 'NeMo'
            - 'Nvidia'
    selection_suspicious_child:
        Image|endswith:
            - '/bin/sh'
            - '/bin/bash'
            - '/bin/python'
            - '/usr/bin/curl'
            - '/usr/bin/wget'
            - '/usr/bin/base64'
        CommandLine|contains:
            - 'curl'
            - 'wget'
            - '/tmp/'
            - 'base64'
            - 'chmod'
            - 'nc '
            - 'nc.traditional'
    condition: selection_parent_process and selection_suspicious_child
falsepositives:
    - Legitimate model-serving frameworks that spawn helper processes
    - Package installation executed from data-science notebooks
    - Anti-virus or backup agents invoking subprocesses
level: high

YARA Rule

// YARA rule to detect serialized payloads containing artifacts
// consistent with unsafe (pickle-based) deserialization targeting
// Nvidia NeMo Megatron Bridge. Flags common pickle opcode (GLOBAL,
// REDUCE) leads, as well as in-band module imports often used for
// gadget chain construction.
rule CVE_2026_61762_Nemo_Megatron_Unsafe_Pickle_Payload
{
    meta:
        author = "Valtersit Threat Intelligence"
        date = "2026-06-01"
        description = "Detects pickle-serialized blobs (protocol 0-5, including compressed/raw opcode streams) that invoke GLOBAL/REDUCE opcodes referencing common code-execution gadget modules."
        cve = "CVE-2026-61762"
        severity = "high"
        reference = "https://nvd.nist.gov/vuln/detail/CVE-2026-61762"

    strings:
        $pickle_header_p0 = "\x80\x00"      // protocol 0
        $pickle_header_p1 = "\x80\x01"      // protocol 1
        $pickle_header_p2 = "\x80\x02"      // protocol 2
        $pickle_header_p3 = "\x80\x03"      // protocol 3
        $pickle_header_p4 = "\x80\x04"      // protocol 4
        $pickle_header_p5 = "\x80\x05"      // protocol 5
        $global_opcode = "\x63"             // c = GLOBAL opcode
        $reduce_opcode = "\x52"             // R = REDUCE opcode
        $build_opcode  = "\x62"             // b = BUILD opcode

        $suspicious_module_os = "os\nsystem"
        $suspicious_module_subprocess = "subprocess"
        $suspicious_module_sh = "posix\nsystem"
        $suspicious_module_builtin = "builtins\neval"
        $suspicious_module_builtin2 = "builtins\nexec"
        $suspicious_module_popen = "subprocess\nPopen"
        $suspicious_module_cmdexec = "commands\ngetoutput"
        $suspicious_module_pyobj = "torch\nload"
        $suspicious_module_nemo = "nemo\n"

    condition:
        uint32(0) == 0x04034b50  // zip (torch.save default) container
        or uint32(0) == 0x00000800  // possible tar container (magic offset)
        or uint32(0) == 0x8b1f      // gzip magic
        and 2 of ($pickle_header*)
        and ($global_opcode or $reduce_opcode or $build_opcode)
        and 1 of ($suspicious_module*)
}

Suricata IDS Rule

# Network detection is limited because CVE-2026-61762 is locally triggered;
# however, distributed/registry-based attacks may pull poisoned artifacts over
# SMB/HTTP/HTTPS. This rule flags known malicious artifact delivery patterns
# that specifically target NeMo/Megatron Bridge download endpoints.
alert http any any -> any any (msg:"CVE-2026-61762 - Potential malicious model artifact download for NeMo Megatron Bridge (pickle/checkpoint)"; flow:established,to_client; content:"Content-Type|3a| application/octet-stream"; http_header; nocase; content:".ckpt"; http_uri; nocase; content:"GET"; http_method; offset:0; pcre:"/(nemo|megatron|model|checkpoint)/i"; classtype:attempted-user; sid:202661762; rev:1; metadata:created_at 2026_06_01, updated_at 2026_06_01, cve CVE-2026-61762;)

# Detection of network exfiltration following exploit: process spawning
# an outbound connection immediately after a parent model load event
# is more accurately caught by EDR; this rule detects suspicious SMB reads
# of checkpoint files from unscoped shares (attack surface indicator only).
alert smb any any -> any any (msg:"CVE-2026-61762 - SMB read of high-risk model checkpoint file extension"; content:"|2e|ckpt"; smb_file_name; nocase; classtype:attempted-user; sid:202661763; rev:1; metadata:cve CVE-2026-61762;)

Elastic Detection

// EQL detection: parent Python-type process (NeMo/Megatron Bridge load)
// followed shortly by a child process executing system shell primitives,
// command-and-control retrieval, or base64-obfuscated command execution.
sequence by process.entity_id with maxspan=30s
  [process where event.action == "exec" and process.name : "python*"
   and process.command_line.text : ("*nemo*", "*megatron*", "*NeMo*")
   and process.args : ("*.ckpt", "*.safetensors", "*.pt", "*.pth", "*.bin")]
  [process where event.action == "exec" and process.name in
     ("curl", "wget", "bash", "sh", "base64", "nc", "python*", "perl", "ruby")
   and process.command_line.text : ("*curl*", "*wget*",
      "*base64*", "*chmod +x*", "*/tmp/*", "*nc *", "*exec*", "*eval*")
   and process.parent.name : "python*"]

Splunk SPL Query

index=endpoint sourcetype=XmlWinEventLog:ProcessCreation OR sourcetype=linux_audit OR sourcetype=ProcessRollup2
| where match(ParentImage, "(?i)python|torch|nemo")
   and match(ProcessImage, "(?i)(curl|wget|bash|sh|base64|nc|python)")
   and (match(CommandLine, "(?i)(curl|wget|base64|chmod|/tmp/|exec|eval)")
        or match(ParentCommandLine, "(?i)(megatron|nemo|nvidia)"))
| eval threat_category = "CVE-2026-61762-UnsafeDeserialization"
| eval alert_severity = "high"
| table _time, host, user, ParentImage, ParentCommandLine, ProcessImage,
        CommandLine, threat_category, alert_severity
| sort - _time

Wazuh OSSEC Rule

<group name="nvidia,deserialization,cve-2026-61762,">
  <!-- Rule triggers on process creation evidence of Python-based model
       loading (NeMo/Megatron) followed by suspicious shell/child activity -->
  <rule id="202617" level="12" frequency="2" timeframe="60">
    <if_matched_group>process_creation</if_matched_group>
    <field name="win.event.data.parentImage">(?i)(python|nemo|megatron)</field>
    <field name="win.event.data.image">(?i)(cmd.exe|powershell.exe|wscript.exe|cscript.exe|bash|sh|curl|wget)</field>
    <description>CVE-2026-61762: Suspicious child process spawned after NeMo/Megatron model deserialization (Python)</description>
    <mitre>
      <id>T1203</id>
    </mitre>
    <group>deserialization_attempt,python,gpu_ml_workload,</group>
  </rule>

  <!-- File-system watch rule: detection of new or modified checkpoint /
       model artifact files arriving on endpoints where Megatron/Nemo is executed -->
  <rule id="202618" level="8">
    <if_sid>550</if_sid>
    <field name="file">\.(ckpt|safetensors|pth|pt|bin)$</field>
    <description>CVE-2026-61762: New/modified serialized model file (potential malicious delivery).</description>
    <group>file_system_artifact_cve_2026_61762,</group>
  </rule>
</group>

Mitigation & Patch Guidance

Patch Confidence is 0/4 — unpatched — with no evidence of a fix from any tracked source (vendor fix: N/A; workarounds: N/A from vendor). The following steps are compiled to reduce exposure pending a vendor release.

Immediate Mitigations (allowed pre-patch)

1. Restrict Deserialization Sources. Do not load model checkpoints/artifacts from untrusted or community repositories. Enforce mandatory ingestion via approved internal artifact registries with cryptographic integrity verification (signed manifest / hash matching) so that any modified payload is detected before deserialization.
2. Sandbox the Bridge Process. Run NeMo Megatron Bridge / model-loading workflows inside an isolated container or virtual machine with least privilege, no write access to the host filesystem, no access to cloud credentials, and egress disabled to the internet.
3. Least-Privilege Execution Account. Ensure the process performing deserialization does not run as root or with GPU-operator / host-level privileges.
4. Monitor High-Signal Telemetry. Deploy the above detection rules on all endpoints that host NeMo/Megatron Bridge environments. Enable auditd/Windows Event Log process tracking to capture the parent-child chain.

Patch / Upgrade Steps

• Current status: NONE — no patched version available. Track Nvidia Security Bulletin RSS and the NVD/NIST page for CVE-2026-61762 daily.
• When a fix is released, apply it to all versions of NeMo Megatron Bridge and the underlying serialization libraries (e.g., PyTorch model-loading utilities) that the organisation uses.
• Test the patch in a staging environment that mirrors the production ML pipeline prior to production rollout.

Long-Term Hardening

1. Replace pickle-based loading with safe formats (e.g., .safetensors or JSON-based config blobs where possible) for external/untrusted models; never use pickle / torch.load without an integrity check.
2. Integrity-Aware Model Registry: Require signed model artifacts and implement runtime verification before deserialization.
3. Vendor Contact: Engage NVIDIA field security contacts (if in your partner channel) to accelerate fix communication and obtain technical addenda if disclosed.

References

1. NIST NVD entry for CVE-2026-61762 — https://nvd.nist.gov/vuln/detail/CVE-2026-61762
2. NVIDIA Product Security Page — https://www.nvidia.com/security/ (monitor for bulletin referencing NeMo Megatron Bridge)
3. MITRE ATT&CK T1203 (Exploitation for Client Execution) — https://attack.mitre.org/techniques/T1203/
4. CWE-502 – Deserialization of Untrusted Data — https://cwe.mitre.org/data/definitions/502.html
5. CISA Known Exploited Vulnerabilities Catalog — https://www.cisa.gov/known-exploited-vulnerabilities-catalog (confirmed not listed for this CVE as of report date)

Report classification: TLP:CLEAR. Prepared 2026-06-01 by Valtersit Threat Intelligence. Updates will be issued upon disclosure of additional technical detail, vendor advisory, or evidence of exploitation in the wild.