CVE-2026-7362
Buffer Overflow in Training data pipeline manipulation allowing adversarial model weight poisoning in Ollama Prompt Router
Executive Summary
CVE-2026-7362 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."
What is this vulnerability?
CVE-2026-7362 is categorized as a critical Buffer Overflow flaw with a CVSS base score of 9.7. 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 Ollama Prompt Router. Specifying as training data pipeline manipulation allowing adversarial model weight poisoning in ollama prompt router, 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
| Metric | Value |
|---|---|
| CVSS Base Score | 9.7 (CRITICAL) |
| Vector String | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H |
| Published | January 13, 2026 |
| Last Modified | January 13, 2026 |
| Related CWEs | CWE-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-2026-7362
- Migrate to Safetensors: Replace pickle-based weights format (.pt, .pkl) with the safe tensor storage format.
- Disable Remote Code Execution: Lock down the loader configuration using
trust_remote_code=False. - 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.
Vulnerability Code Signature
Attack Data Flow
| Stage | Detail |
|---|---|
| Source | Network packet or file input |
| Vector | Data exceeds the allocated buffer bounds during a copy operation |
| Sink | strcpy(), memcpy(), or pointer arithmetic |
| Impact | Memory 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.