CVE-2026-7593

Buffer Overflow in Prompt injection vulnerability in agentic tools allowing unauthorized shell execution in MLflow Weights Parser

Verified by Precogs Threat Research
Last Updated: Jan 13, 2026
Base Score
9.0CRITICAL

Executive Summary

CVE-2026-7593 is a critical severity vulnerability affecting aibom. It is classified as Memory Buffer Overflow. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.

Precogs AI Insight

"Precogs AI scans model weights and configurations to detect insecure serialization (e.g. PyTorch pickle structures) and prompt escapes, reinforcing the AIBOM validation pipeline."

Exploit Probability (EPSS)
Low (2.1%)
Public POC
Available
Exploit Probability
High (84%)
Public POC
Available
Affected Assets
aibomCWE-119

What is this vulnerability?

CVE-2026-7593 is categorized as a critical Buffer Overflow flaw with a CVSS base score of 9.0. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A security exposure has been identified in MLflow Weights Parser. Specifying as prompt injection vulnerability in agentic tools allowing unauthorized shell execution in mlflow weights parser, this vulnerability enables remote or local actors to exploit bounds or logical checks.

This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.

Risk Assessment

MetricValue
CVSS Base Score9.0 (CRITICAL)
Vector StringCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
PublishedJanuary 13, 2026
Last ModifiedJanuary 13, 2026
Related CWEsCWE-119

Impact on Systems

Remote Code Execution: Untrusted serialization loading triggers shellcode execution in inference workers.

Model Poisoning: Modified weights trigger biased or malicious outputs under target conditions.

Data Exfiltration: Manipulated chat or pipeline contexts leak system prompts or fine-tuning datasets.

How to Fix and Mitigate CVE-2026-7593

  1. Migrate to Safetensors: Replace pickle-based weights format (.pt, .pkl) with the safe tensor storage format.
  2. Disable Remote Code Execution: Lock down the loader configuration using trust_remote_code=False.
  3. Integrate AIBOM Logs: Implement cryptographic verification signatures on all imported models and pipelines.

Defending with Precogs AI

Precogs AI scans model weights and configurations to detect insecure serialization (e.g. PyTorch pickle structures) and prompt escapes, reinforcing the AIBOM validation pipeline.

Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.

Start scanning with Precogs →

Vulnerability Code Signature

Attack Data Flow

StageDetail
SourceNetwork packet or file input
VectorData exceeds the allocated buffer bounds during a copy operation
Sinkstrcpy(), memcpy(), or pointer arithmetic
ImpactMemory corruption, Remote Code Execution (RCE)

Vulnerable Code Pattern

// ❌ VULNERABLE: Memory Buffer Overflow
void process_data(char *input) {
    char buffer[64];
    // Taint sink: copies without bounds checking
    strcpy(buffer, input);
}

Secure Code Pattern

// ✅ SECURE: Bounded copy
void process_data(char *input) {
    char buffer[64];
    // Sanitized boundary check
    strncpy(buffer, input, sizeof(buffer) - 1);
    buffer[sizeof(buffer) - 1] = '\0';
}

How Precogs Detects This

Precogs Binary SAST engine explicitly uncovers memory boundary violations and unsafe memory management functions in compiled binaries.

Related Vulnerabilitiesvia CWE-119

Is your system affected?

Precogs AI detects CVE-2026-7593 in compiled binaries, LLMs, and application layers — even without source code access.