CVE-2026-0599 — HIGH (CVSS 7.5) AI Security Vulnerability

Q: Is CVE-2026-0599 actively exploited?

No confirmed active exploitation of CVE-2026-0599 has been reported, but organizations should still patch proactively.

Q: How to fix CVE-2026-0599?

1. PATCH: Upgrade to text-generation-inference 3.3.7 immediately — this is the definitive fix. 2. INTERIM if patching is blocked: Enable API authentication via --authentication-config flag to require bearer tokens; this prevents unauthenticated exploitation. 3. ADD EGRESS CONTROLS: Restrict outbound HTTP from the TGI process/container to internal or whitelisted endpoints only — this breaks the attack chain by preventing external image fetching. 4. ENFORCE MEMORY LIMITS: Set container memory limits (Docker: --memory=Xg, Kubernetes: resources.limits.memory) to contain blast radius and prevent host OOM. 5. DEPLOY API GATEWAY: Place TGI behind an API gateway or reverse proxy with rate limiting and request body size limits (e.g., nginx client_max_body_size, Kong rate-limit plugin). 6. DETECTION: Alert on anomalous memory growth spikes in inference containers, unusual outbound bandwidth from inference pods, and repeated 429/413 response codes paired with sustained resource utilization.

Q: What systems are affected by CVE-2026-0599?

This vulnerability affects the following AI/ML architecture patterns: multimodal/VLM inference serving, LLM inference servers, model serving, self-hosted AI APIs, AI agent frameworks with vision capabilities.

Q: What is the CVSS score for CVE-2026-0599?

CVE-2026-0599 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.26%.

CISO Take

If you're running HuggingFace TGI in VLM (multimodal) mode, patch to 3.3.7 now — this is a trivial, unauthenticated DoS that can crash your inference host with a single crafted request. Default deployments have no memory limits and no authentication, meaning your entire AI inference stack is one HTTP request away from an OOM crash. Treat this as critical if your AI pipelines serve multimodal workloads without an auth layer or network egress controls.

Risk Assessment

Effective risk is higher than CVSS 7.5 suggests for AI-specific deployments. The attack requires zero credentials, zero AI/ML knowledge, and zero user interaction — just a POST with a Markdown image URL pointing to a large resource. Default TGI deployments (as documented by HuggingFace) expose the inference API without authentication, maximizing blast radius. EPSS of 0.00245 indicates limited observed exploitation, but the technique is trivially discoverable. Organizations running multimodal LLM inference at scale face compounded risk: a single attacker can saturate bandwidth, exhaust memory, and spike CPU simultaneously, crashing the host before any rate limiting or token validation kicks in.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
text-generation	pip	< 3.3.7	`3.3.7`

Do you use text-generation? You're affected.

Severity & Risk

CVSS 3.1

7.5 / 10

EPSS

0.3%

chance of exploitation in 30 days

Higher than 49% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

Recommended Action

6 steps

PATCH

Upgrade to text-generation-inference 3.3.7 immediately — this is the definitive fix.
INTERIM if patching is blocked: Enable API authentication via --authentication-config flag to require bearer tokens; this prevents unauthenticated exploitation.
ADD EGRESS CONTROLS

Restrict outbound HTTP from the TGI process/container to internal or whitelisted endpoints only — this breaks the attack chain by preventing external image fetching.
ENFORCE MEMORY LIMITS

Set container memory limits (Docker: --memory=Xg, Kubernetes: resources.limits.memory) to contain blast radius and prevent host OOM.
DEPLOY API GATEWAY

Place TGI behind an API gateway or reverse proxy with rate limiting and request body size limits (e.g., nginx client_max_body_size, Kong rate-limit plugin).
DETECTION

Alert on anomalous memory growth spikes in inference containers, unusual outbound bandwidth from inference pods, and repeated 429/413 response codes paired with sustained resource utilization.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable Yes

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

DoS Inference Framework API AML.T0006 - Active Scanning AML.T0029 - Denial of AI Service AML.T0034 - Cost Harvesting AML.T0040 - AI Model Inference API Access AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2 - AI system operational management A.9.2 - AI System Availability and Resilience

NIST AI RMF

GOVERN 1.1 - AI risk is integrated into organizational risk management GOVERN 1.4 - Risks associated with AI system vulnerabilities are identified and managed MANAGE 2.2 - Mechanisms to detect, respond to, and recover from AI system failures

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2026-0599?

If you're running HuggingFace TGI in VLM (multimodal) mode, patch to 3.3.7 now — this is a trivial, unauthenticated DoS that can crash your inference host with a single crafted request. Default deployments have no memory limits and no authentication, meaning your entire AI inference stack is one HTTP request away from an OOM crash. Treat this as critical if your AI pipelines serve multimodal workloads without an auth layer or network egress controls.

Is CVE-2026-0599 actively exploited?

No confirmed active exploitation of CVE-2026-0599 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-0599?

1. PATCH: Upgrade to text-generation-inference 3.3.7 immediately — this is the definitive fix. 2. INTERIM if patching is blocked: Enable API authentication via --authentication-config flag to require bearer tokens; this prevents unauthenticated exploitation. 3. ADD EGRESS CONTROLS: Restrict outbound HTTP from the TGI process/container to internal or whitelisted endpoints only — this breaks the attack chain by preventing external image fetching. 4. ENFORCE MEMORY LIMITS: Set container memory limits (Docker: --memory=Xg, Kubernetes: resources.limits.memory) to contain blast radius and prevent host OOM. 5. DEPLOY API GATEWAY: Place TGI behind an API gateway or reverse proxy with rate limiting and request body size limits (e.g., nginx client_max_body_size, Kong rate-limit plugin). 6. DETECTION: Alert on anomalous memory growth spikes in inference containers, unusual outbound bandwidth from inference pods, and repeated 429/413 response codes paired with sustained resource utilization.

What systems are affected by CVE-2026-0599?

This vulnerability affects the following AI/ML architecture patterns: multimodal/VLM inference serving, LLM inference servers, model serving, self-hosted AI APIs, AI agent frameworks with vision capabilities.

What is the CVSS score for CVE-2026-0599?

CVE-2026-0599 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.26%.

Technical Details

NVD Description

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

Exploitation Scenario

An attacker identifies a publicly accessible TGI endpoint running in VLM mode — discoverable via Shodan or by probing common ports (8080, 8000) with the /info endpoint. They craft a POST to /generate containing a prompt with a Markdown image reference: `What do you see? ![img](http://attacker-controlled.com/10gb-random.bin)`. The TGI router parses the Markdown, initiates a blocking HTTP GET to the attacker's server, and streams the full 10GB response into memory before any token-limit validation occurs. The attacker runs this concurrently from multiple IPs or even a single client with multiple threads. Within seconds to minutes (depending on bandwidth), the TGI process exhausts available RAM, triggering OOM kills and crashing the inference service — with no authentication required and no prior knowledge of the model or API needed.