CVE-2025-52645: HCLTech AION Model Integrity Vulnerability

CVE-2025-52645 Overview

CVE-2025-52645 affects HCL AION, where model packaging and distribution mechanisms lack sufficient authenticity verification. Attackers can substitute or modify model artifacts without detection, undermining the integrity of machine learning models delivered through the platform. The flaw is categorized under [CWE-345] Insufficient Verification of Data Authenticity.

The vulnerability is network-accessible and requires no privileges or user interaction. Successful exploitation does not expose confidential data or disrupt availability, but it permits unverified or tampered model artifacts to enter the pipeline. This can produce unintended model behavior in downstream applications relying on AION-distributed artifacts.

Critical Impact
Tampered AI model artifacts may be loaded and executed by consumers of HCL AION, leading to integrity violations and unintended model behavior in production AI workflows.

Affected Products

HCL AION (all versions covered by vendor advisory KB0129410)
Deployments using AION model packaging and distribution components
Downstream systems consuming AION-distributed model artifacts

Discovery Timeline

2026-03-16 - CVE-2025-52645 published to the National Vulnerability Database
2026-04-25 - Last updated in NVD database

Technical Details for CVE-2025-52645

Vulnerability Analysis

The vulnerability resides in the model packaging and distribution workflow of HCL AION. The platform does not adequately verify the authenticity of model artifacts before they are consumed. Without strong authenticity checks such as cryptographic signatures or trusted publisher validation, the system cannot distinguish a legitimate model from one that has been altered.

An attacker positioned to introduce or modify a model artifact in transit or at rest can substitute their own version. Consumers ingesting the artifact through standard AION distribution channels will treat the malicious artifact as trusted. The result is a supply-chain risk specific to AI/ML pipelines, where compromised models can alter inference outputs, bias predictions, or embed hidden behaviors.

The issue affects integrity only. Confidentiality and availability are not directly impacted, which aligns with the limited scope of the trust failure rather than a memory safety or access control flaw.

Root Cause

The root cause is the absence or insufficiency of authenticity verification, mapped to [CWE-345]. Model packages are accepted and distributed without robust validation of their origin or contents. Trust is implicit rather than enforced through verifiable signatures or attestations.

Attack Vector

The attack vector is network-based. An attacker with the ability to influence the model packaging, distribution channel, or artifact storage can introduce a modified model. Because no user interaction or privileges are required, exploitation can occur silently during normal model retrieval. Defenders should treat any tampering as undetectable without external verification.

No public proof-of-concept code is available for this issue. Refer to the HCL Software Knowledge Base Article for vendor technical guidance.

Detection Methods for CVE-2025-52645

Indicators of Compromise

Model artifact hashes that do not match values recorded by trusted internal build systems or vendor-published references.
Unexpected changes to model behavior, accuracy, or output distributions after artifact updates.
Model files retrieved from AION with timestamps, sizes, or metadata inconsistent with authorized release records.

Detection Strategies

Compute and compare cryptographic hashes (SHA-256) of model artifacts against an independent, out-of-band manifest before loading.
Maintain an inventory of authorized model versions and alert on any artifact deployed outside that inventory.
Inspect AION distribution logs for artifact uploads or replacements originating from unexpected accounts or pipelines.

Monitoring Recommendations

Enable detailed audit logging for all model packaging, upload, and distribution events within AION.
Forward AION and model registry telemetry to a centralized SIEM for correlation with identity and network activity.
Monitor model inference output drift as a behavioral signal that an artifact may have been tampered with.

How to Mitigate CVE-2025-52645

Immediate Actions Required

Review the vendor guidance in HCL Knowledge Base article KB0129410 and apply the fixed AION version as instructed.
Restrict who can publish or modify model artifacts in AION to a minimal set of authorized identities.
Validate currently deployed model artifacts against trusted hashes before continued use in production inference.

Patch Information

HCL has published remediation details in the HCL Software Knowledge Base Article. Administrators should follow the vendor's documented upgrade path and configuration steps to enable stronger authenticity verification on model packaging and distribution.

Workarounds

Sign model artifacts out-of-band and require signature verification at the consumer before any model is loaded.
Transport model artifacts only over authenticated, mutually-TLS protected channels between trusted endpoints.
Pin model versions and hashes in deployment manifests so that any substitution causes deployment failure rather than silent acceptance.

bash

# Example: verify a model artifact hash before loading
EXPECTED_SHA256="<hash-from-trusted-manifest>"
ACTUAL_SHA256=$(sha256sum ./model.pkg | awk '{print $1}')
if [ "$ACTUAL_SHA256" != "$EXPECTED_SHA256" ]; then
  echo "Model artifact failed authenticity check - aborting load"
  exit 1
fi