CVE‑2026‑42810: Apache Polaris Wildcard Injection Flaw – What It Means for Your Business and How to Respond

Intro: Why This CVE Matters

CVE‑2026‑42810 is a critical‑severity vulnerability in Apache Polaris that allows attackers with low‑privileged access to reach sensitive data stored in S3 across multiple tables, even when they should not have that permission. This affects organizations in the United States and Canada that rely on Polaris for cloud‑native analytics, data‑warehousing, and governance, especially those using AWS S3 or compatible storage such as MinIO. If your business runs Polaris 1.4.0 or earlier, you may be exposed to unauthorized data access, compliance breaches, and long‑term reputational damage. This post explains what this flaw means for your operations, how to quickly determine whether you are affected, and what concrete steps your team can take to reduce risk and accelerate remediation.

Background & History

CVE‑2026‑42810 was disclosed in May 2026 and is classified as a Critical‑severity vulnerability, with CVSS scores ranging from 9.4 to 10.0 depending on the configuration. The issue affects Apache Polaris, a metadata and governance layer used to manage cloud data lakes and analytics workloads, particularly when it issues temporary AWS S3 credentials to catalog tables. The vulnerability is rooted in improper input validation when handling table and namespace names that contain wildcards, such as *. Security researchers reported that specially crafted table names can cause Polaris to generate S3 credentials that match unrelated tables’ storage paths, effectively broadening the scope of delegated access. No widespread public exploits have been observed yet, but the flaw is attractive to attackers because it can be triggered over the network with low attack complexity and does not require user interaction once an attacker has access to a Polaris‑backed environment.

What This Means for Your Business

If your organization runs affected Polaris versions in production, CVE‑2026‑42810 can undermine confidentiality, compliance, and trust in your data environment. An attacker with low‑privileged access to your Polaris catalog can potentially read or manipulate data belonging to other teams, departments, or even different customers, depending on how your data lake is partitioned. This increases the risk of data‑exposure incidents that may trigger regulatory scrutiny, especially under frameworks like GDPR, HIPAA, CCPA, and Canadian privacy laws if personally identifiable or sensitive health or financial information is stored in exposed S3 tables. In a worst‑case scenario, attackers could copy or exfiltrate sensitive data across tables, disrupt analytics workflows, or plant misleading results that impact business decisions. Beyond technical risk, your organization may face reputational damage, loss of customer trust, and increased due‑diligence burden from partners and auditors who ask whether you have addressed this class of wildcard‑based credential‑broadening flaws.

Real‑World Examples

Healthcare analytics provider: A regional healthcare analytics provider in the United States uses Polaris to manage a multi‑tenant data lake that ingests claims data from multiple insurers. If an attacker gains access to one tenant’s analytics workspace, CVE‑2026‑42810 could allow them to obtain credentials that also reach other tenants’ S3 prefixes, potentially exposing PHI‑related datasets and triggering a HIPAA‑related breach investigation and costly remediation.

E‑commerce platform in Canada: A Canadian e‑commerce platform uses Polaris to orchestrate customer‑behavior and product‑catalog analytics across regional S3 prefixes. A low‑privileged user with table access in one analytics environment could exploit this vulnerability to read or overwrite data from other regions’ tables, leading to data‑integrity issues, skewed marketing reports, and potential violations of Canadian privacy regulations.

Financial services firm: A mid‑size US financial services firm relies on Polaris to secure access to credit‑risk and transaction‑history datasets. If an attacker compromises a developer or analyst account, they could use wildcard‑bearing table names to escalate their access to S3 paths containing core risk models and customer data, substantially increasing the impact of an otherwise limited compromise.

Media and streaming company: A North American media and streaming company uses Polaris‑managed S3 tables to store viewer‑behavior logs and recommendation‑engine inputs. Exploitation of this flaw could allow an attacker to access behavioral data across multiple regional tables, enabling targeted profiling or blackmail‑style extortion campaigns that leverage highly sensitive user‑activity patterns.

Am I Affected?

• You should assume you may be affected by CVE‑2026‑42810 if:

• You are running Apache Polaris version 1.4.0 or an earlier release in any environment that delegates AWS S3 or S3‑compatible object‑storage credentials.

• Your Polaris‑managed tables or namespaces can contain wildcard‑style characters such as * in their names or paths.

• Your S3‑based IAM policies or temporary‑credential workflows rely on simple prefix‑ or wildcard‑based matching, without explicit escaping or strict validation of table‑to‑path mappings.

• Your data lake or analytics platform exposes Polaris‑backed catalogs to multiple teams, departments, or external partners, increasing the number of potential low‑privileged accounts that could be abused.

If any of these conditions apply, treat this CVE as a high‑priority item for your security and cloud‑engineering teams.

Key Takeaways

• CVE‑2026‑42810 in Apache Polaris can allow low‑privileged users to obtain S3 credentials that reach tables they should not be able to access, creating significant data‑exposure and compliance risk.

• Organizations in the United States and Canada that run Polaris‑managed data lakes, especially those handling healthcare, financial, or customer‑behavior data, face elevated operational and regulatory exposure.

• Immediate patching to a vendor‑recommended Polaris version that corrects the wildcard‑handling logic is the most effective way to remove this specific risk.

• Until patches are fully deployed, tightening S3‑IAM policies, auditing table‑to‑prefix mappings, and limiting analyst and developer access to only the tables they actually need can meaningfully reduce the attack surface.

• A comprehensive vulnerability‑management and penetration‑testing program can help identify similar input‑validation and credential‑delegation weaknesses before attackers do.

Call to Action:

If your organization operates in the US or Canada and relies on Polaris or similar cloud‑native analytics platforms, now is the time to validate your exposure and strengthen your overall data‑security posture. Contact IntegSec at https://integsec.com to schedule a tailored penetration test and deep‑dive cybersecurity assessment. Our team will help you map your Polaris‑based environments, identify critical vulnerabilities like CVE‑2026‑42810, and implement long‑term controls that reduce risk across your cloud, data, and application landscape.

TECHNICAL APPENDIX (security engineers, pentesters, IT professionals only)

A — Technical Analysis

CVE‑2026‑42810 is an improper input‑validation issue in Apache Polaris 1.4.0 that stems from how wildcard‑bearing table and namespace names are processed when generating temporary AWS S3 credentials. The vulnerability resides in Polaris’ S3 credential‑delegation logic, where a crafted table name (for example, f*.t1, foo.*, or *.*) can cause the issued credentials to match S3 paths belonging to other, unrelated tables. This effectively allows an attacker to “broaden” access beyond the intended table prefix, turning a low‑privileged account into a vector for cross‑table S3 access. The attack vector is network‑based, requires low privileges, and does not depend on user interaction, leading to CVSS‑4.0 scores in the 9.4–10.0 range and consistent “Critical” severity labeling by major vendors. The underlying weakness is classified as an input‑validation flaw (CWE‑20) in the context of credential‑delegation and wildcard‑based path matching, with confirmed impact on confidentiality, integrity, and availability of S3‑backed tables. Public references include the NVD entry for CVE‑2026‑42810 and vendor‑specific advisories from Polaris maintainers and cloud‑security vendors.

B — Detection & Verification

To detect whether CVE‑2026‑42810 is present in an environment, security teams should first verify Polaris version and S3‑interaction patterns. Version enumeration can be performed by querying the Polaris API endpoint or inspecting the running container image or package metadata, for example with commands such as curl -s <polaris‑url>/v1/version or docker inspect <polaris‑container> | grep -i polaris. Many vulnerability scanners and security‑information platforms now include signatures for Polaris 1.4.0‑related S3 credential‑delegation issues, and log‑monitoring rules can flag unusual S3 prefix‑based access patterns, such as a single IAM role or temporary credential accessing multiple unrelated table prefixes in a short time window. Behavioral indicators include S3 GetObject, ListObject, PutObject, or DeleteObject calls from roles that should only operate within a single table’s prefix, as well as API calls that originate from table‑creation or metadata‑view operations involving wildcard‑bearing names. Network‑level indicators may include repeated API calls from Polaris‑managed hosts to S3 endpoints with paths that diverge from expected table‑to‑prefix mappings, especially when the source account is a low‑privileged user.

C — Mitigation & Remediation

1. Immediate (0–24h):

• Identify all Polaris‑managed clusters and confirm whether they run Polaris 1.4.0 or earlier; take affected instances out of production or behind strict access controls if patching cannot be applied immediately.

• Restrict network access to Polaris‑managed S3 endpoints so that only authorized service accounts and IP ranges can reach the affected buckets, and temporarily disable or tightly scope any wildcard‑based table‑name creation workflows.

• Rotate S3 access keys and short‑term credentials used by Polaris‑integrated services, and adjust IAM policies to explicitly deny ListBucket and PutObject permissions on prefixes that should not be accessible to low‑privileged roles.

2. Short‑term (1–7d):

• Upgrade Polaris to the latest vendor‑recommended version that includes a fix for the wildcard‑handling and credential‑delegation issue, following the official patch guidance and release notes.

• Re‑validate all table‑to‑prefix mappings in Polaris and S3 so that no wildcard‑based credentials can inadvertently match unrelated tables; document and enforce a naming‑policy standard that disallows wildcard‑bearing table and namespace names in production.

• Run a vulnerability scan or pentest focused on Polaris‑managed environments to confirm that no other similar input‑validation or credential‑broadening flaws exist in your catalog‑to‑storage layer.

3. Long‑term (ongoing):

• Implement a formal policy that requires all table‑to‑S3‑prefix mappings to be validated and audited before deployment, with automated checks that reject wildcard‑bearing names or enforce strict escaping rules.

• Integrate Polaris into your continuous vulnerability‑management workflow, ensuring that new versions are evaluated for similar input‑validation and credential‑delegation issues before promotion to production.

• Maintain a least‑privilege IAM model for S3, using explicit prefixes, deny‑over‑allow patterns, and time‑bound temporary credentials, so that even if a future flaw allows credential‑scope creep, its blast radius is minimized.

D — Best Practices

• Design and enforce naming conventions for tables and namespaces that prohibit wildcard‑style characters in production environments, and validate all table‑name inputs at the application layer.

• Use IAM policies that explicitly list allowed S3 prefixes and explicitly deny access to unrelated prefixes, instead of relying on broad wildcard‑based allows.

• Implement continuous logging and monitoring of S3 API calls to detect anomalous cross‑table access patterns and correlate them with Polaris‑related table‑creation or metadata operations.

• Harden your Polaris‑based environments by segmenting multi‑tenant workloads into separate catalogs or clusters and applying strict network‑ and role‑based access controls between them.

• Conduct periodic penetration tests and security‑code reviews on your Polaris‑integrated data‑governance stack to uncover similar input‑validation and credential‑delegation weaknesses before they can be exploited.