CVE-2018-7577 — UNKNOWN AI Security Vulnerability

Q: Is CVE-2018-7577 actively exploited?

No confirmed active exploitation of CVE-2018-7577 has been reported, but organizations should still patch proactively.

Q: How to fix CVE-2018-7577?

1. PATCH: Upgrade TensorFlow to >= 1.7.1 immediately on all inference servers, training workers, and notebook environments. 2. AUDIT: Inventory all containers and VMs with pinned TF 1.x versions — check Dockerfile FROM lines and requirements.txt/environment.yml files. 3. ISOLATION: If upgrade is not immediately possible, ensure TF serving endpoints do not accept externally-supplied compressed payloads directly; add input validation at the API gateway layer. 4. DETECT: Monitor for unexpected process crashes in TF serving pods — repeated crashes of ML serving containers may indicate exploitation attempts. 5. SBOM: Validate that no internal ML platform bundles Snappy 1.1.4 as a transitive dependency outside of TF (e.g., Apache Arrow, LevelDB wrappers).

CISO Take

TensorFlow deployments prior to 1.7.1 are vulnerable to a memory safety bug in the bundled Snappy compression library that can crash the process or leak memory contents. Any legacy TF inference server or training pipeline still on TF <1.7.1 should be treated as a priority upgrade — the attack surface is any code path that decompresses Snappy-encoded data (TFRecords, checkpoints, protocol buffers). Modern TF deployments (1.7.1+) are not affected; audit your model-serving and data-pipeline containers for pinned legacy versions.

Risk Assessment

Risk is LOW for organizations running current TensorFlow versions but HIGH for legacy deployments common in long-lived ML infrastructure. No CVSS score is officially assigned, but the underlying flaw (CWE-20 improper input validation leading to overlapping memcpy) is well-understood and exploitable by crafting malicious compressed input. The memory-read primitive is the more dangerous outcome — it could expose training data, model weights, or credentials resident in process memory. Crash-based DoS is trivially achievable. The 2019 publish date means many organizations may have silently carried this in containerized ML workloads never upgraded past TF 1.x.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
tensorflow	pip	—	No patch
195.0K OpenSSF 7.2 3.7K dependents Pushed 6d ago 4% patched ~1372d to patch Full package profile →
snappy	—	—	No patch

Severity & Risk

CVSS 3.1

N/A

EPSS

0.2%

chance of exploitation in 30 days

Higher than 37% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Moderate

Recommended Action

5 steps

PATCH

Upgrade TensorFlow to >= 1.7.1 immediately on all inference servers, training workers, and notebook environments.
AUDIT

Inventory all containers and VMs with pinned TF 1.x versions — check Dockerfile FROM lines and requirements.txt/environment.yml files.
ISOLATION

If upgrade is not immediately possible, ensure TF serving endpoints do not accept externally-supplied compressed payloads directly; add input validation at the API gateway layer.
DETECT

Monitor for unexpected process crashes in TF serving pods — repeated crashes of ML serving containers may indicate exploitation attempts.
SBOM

Validate that no internal ML platform bundles Snappy 1.1.4 as a transitive dependency outside of TF (e.g., Apache Arrow, LevelDB wrappers).

Classification

DoS Data Extraction Supply Chain Framework Training Data Inference AML.T0010.001 - AI Software AML.T0035 - AI Artifact Collection AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

ISO 42001

8.4 - AI system risk management

NIST AI RMF

GOVERN-1.7 - Processes for AI risk identification and tracking MANAGE-2.2 - Mechanisms to sustain oversight of AI risks

OWASP LLM Top 10

LLM06:2025 - Excessive Agency / Sensitive Information Disclosure

Frequently Asked Questions

What is CVE-2018-7577?

TensorFlow deployments prior to 1.7.1 are vulnerable to a memory safety bug in the bundled Snappy compression library that can crash the process or leak memory contents. Any legacy TF inference server or training pipeline still on TF <1.7.1 should be treated as a priority upgrade — the attack surface is any code path that decompresses Snappy-encoded data (TFRecords, checkpoints, protocol buffers). Modern TF deployments (1.7.1+) are not affected; audit your model-serving and data-pipeline containers for pinned legacy versions.

Is CVE-2018-7577 actively exploited?

No confirmed active exploitation of CVE-2018-7577 has been reported, but organizations should still patch proactively.

How to fix CVE-2018-7577?

1. PATCH: Upgrade TensorFlow to >= 1.7.1 immediately on all inference servers, training workers, and notebook environments. 2. AUDIT: Inventory all containers and VMs with pinned TF 1.x versions — check Dockerfile FROM lines and requirements.txt/environment.yml files. 3. ISOLATION: If upgrade is not immediately possible, ensure TF serving endpoints do not accept externally-supplied compressed payloads directly; add input validation at the API gateway layer. 4. DETECT: Monitor for unexpected process crashes in TF serving pods — repeated crashes of ML serving containers may indicate exploitation attempts. 5. SBOM: Validate that no internal ML platform bundles Snappy 1.1.4 as a transitive dependency outside of TF (e.g., Apache Arrow, LevelDB wrappers).

What systems are affected by CVE-2018-7577?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, data ingestion pipelines, distributed training.

What is the CVSS score for CVE-2018-7577?

No CVSS score has been assigned yet.

Technical Details

NVD Description

Memcpy parameter overlap in Google Snappy library 1.1.4, as used in Google TensorFlow before 1.7.1, could result in a crash or read from other parts of process memory.

Exploitation Scenario

An adversary with access to a TensorFlow model-serving endpoint or a data ingestion pipeline (e.g., a TFRecord upload endpoint) crafts a maliciously overlapping Snappy-compressed payload. When the TF process decompresses this input, the memcpy overlap triggers either a segfault (crashing the serving pod, causing service disruption) or returns memory from adjacent process regions. In a training pipeline scenario, an attacker who can inject poisoned TFRecord files into a shared data lake could trigger the vulnerability during the data-loading phase, potentially reading batch data or model state from memory. In a serving scenario, a single malformed inference request could be sufficient to crash or read memory from a multi-tenant TF Serving instance.