CVE-2026-111170 - kedro-datasets 9.3.0 through 9.6.0 Path Traversal via...

Executive Summary

CVE-2026-111170 (CVSS 6.5) — CVE-2026-111170 - kedro-datasets 9.3.0 through 9.6.0 Path Traversal via MatplotlibDataset Plot Name. Source-attributed analysis and response guidance by CYBERDUDEBIVASH SENTINEL APEX.

Key Judgements

Key Judgements are derived from validated ReportX claim identifiers rather than a second free-form model request. They may prioritize validation and collection, but they do not convert source-reported claims into confirmed customer impact.

Verified Facts

• Source publisher: CYBERDUDEBIVASH
• Source published: 2026-10-16T12:00:00+00:00

Threat Classification

Report family: CVE Vulnerability Advisory. Exploitation state: Unknown — source record does not confirm active exploitation. Remediation state: Availability not established in the source record — verify vendor guidance. These labels come from the normalized report context and remain bounded by cited evidence.

Threat Severity Assessment

Severity is interpreted from structured scoring or source evidence when present. No customer-specific risk score, blast radius, or compromise state is inferred by this compiler.

Evidence & Source Assessment

Primary provenance is CYBERDUDEBIVASH. The compiler distinguishes source-reported material from independently structured NVD/CISA/EPSS evidence when those fields are actually present.

Timeline & Chronology

The normalized source publication timestamp is 2026-10-16T12:00:00+00:00. Additional intrusion, exploitation, disclosure, or remediation timestamps are not asserted unless they are present in the cited evidence body below.

Business Impact

Customer-specific business impact is not established by a public source record alone. Decision value comes from determining whether relevant assets, identities, dependencies, or services are actually present and exposed.

Enterprise Exposure Assessment

• Inventory and ownership teams should determine whether the technology or service named in the cited record exists in the environment before assigning incident status.
• A positive exposure determination requires asset, version, identity, dependency, cloud/SaaS, or equivalent internal telemetry that can be tied to the cited condition.
• A negative determination should record the inventory evidence and timestamp used so the decision can be revalidated if the source record changes.

Technical Analysis

The vulnerability arises from insufficient validation of the plot name parameter in the MatplotlibDataset component. When a malicious plot name containing directory traversal sequences (e.g., “../”) is supplied, the component resolves the path relative to the working directory, potentially accessing files outside the intended dataset directory. This flaw can be exploited to read arbitrary files or, depending on write permissions, to overwrite critical files. The path traversal is not mitigated by any built‑in sanitization or whitelist checks in the affected versions.

**Affected Product/Version**: kedro‑datasets 9.3.0 through 9.6.0.
**Weakness/Root Cause**: Lack of input sanitization for the plot name parameter in MatplotlibDataset.
**Prerequisites**: The application must instantiate a MatplotlibDataset and provide a plot name derived from external or user‑controlled input.
**Reachable AttackReport‑Type Deep Dive

The root cause is identified as insufficient validation of the plot name parameter within the MatplotlibDataset class. The affected versions, 9.3.0 through 9.6.0, fail to sanitize directory traversal sequences, allowing path manipulation. The prerequisite for exploitation is that the application must accept external or user-controlled input for the plot name. If the plot name is hardcoded or strictly validated upstream, the attack surface is significantly reduced. The reachable attack surface depends on how the kedro pipeline is configured; if the dataset is instantiated in a context where the plot name is derived from API parameters, database records, or file uploads, the vulnerability is directly exploitable. The source does not provide a specific fixed version number, so organizations must monitor the kedro-datasets release channel for a patch. Until a patch is confirmed, the primary mitigation is to ensure that plot names are generated internally or strictly validated against a whitelist of allowed characters and path structures. This requires a review of all code paths that instantiate MatplotlibDataset to identify where the plot name originates. If the input is user-controlled, immediate remediation is required to prevent potential file disclosure or corruption. The absence of a disclosed exploit in the wild suggests that the vulnerability may not be widely known, but the technical simplicity of path traversal makes it a high-priority target for automated scanners and opportunistic attackers.

Based on the technical description of path traversal, the primary ATT&CK technique is T1083 (File and Directory Discovery). The attacker uses the vulnerability to enumerate or access files outside the intended directory. If the traversal allows reading sensitive files, it may also map to T1005 (Data from Local System). If the traversal allows writing to files, it could facilitate T1105 (Ingress Tool Transfer) or T1543 (Create or Modify System Process) if the written file is executable or a configuration file. However, the source does not confirm write capabilities, so these mappings are speculative. The primary confirmed mapping is T1083, as the core flaw involves path manipulation to access unintended files. Detection should focus on unusual file access patterns by the application process, specifically access to files outside the expected dataset directory. This requires file integrity monitoring or process-level file access logging. The ATT&CK mapping is limited by the lack of detailed exploit code or PoC in the source, so additional techniques may apply if the vulnerability is used in a more complex attack chain.

• CVE Identifier: CVE-2026-111170.
• Affected Library: kedro-datasets versions 9.3.0 through 9.6.0.
• Vulnerable Component: MatplotlibDataset class.
• Attack Vector: Path traversal via plot name parameter.
• CVSS Score: 6.5.
• Source URL: https://blog.cyberdudebivash.in/posts/cve-2026-111170.html.
• Publisher: CYBERDUDEBIVASH.
• Publication Date: 2026-10-16.

No specific IOCs such as IP addresses, domains, or file hashes are provided in the source. The primary observable is the presence of the vulnerable library version in the software inventory. Detection should focus on the library version and the behavior of the MatplotlibDataset component. Organizations should scan their dependency trees for kedro-datasets versions within the affected range. If the library is present, further investigation is required to determine if the plot name is user-controlled. The absence of specific IOCs limits the ability to detect active exploitation in network logs, so host-based detection is the primary defense. The lack of a known exploit makes it difficult to identify specific attack patterns, so broad monitoring for unusual file access is recommended.

Detection engineering should focus on host-based telemetry, specifically file access events by the application process running kedro-datasets. The primary detection logic

Given the lack of specific exploit code, detection logic must rely on behavioral anomalies rather than signature-based matching. The primary detection strategy involves monitoring file system access events generated by the Python process executing the Kedro pipeline. Specifically, analysts should look for file open operations where the resolved path deviates from the expected dataset directory structure. This requires enabling detailed audit logs on the host running the data pipeline, focusing on system calls related to file reading and writing. Since the vulnerability involves path traversal, the presence of directory traversal sequences in the request parameters or internal function calls is a strong indicator of compromise. However, because the source does not provide specific log formats or event IDs, the detection rule must be tailored to the organization's specific logging infrastructure. For example, if using Windows, Sysmon Event ID 11 (File Create) and Event ID 11 (File Delete) should be monitored for unusual paths. On Linux, auditd rules should be configured to log file access events for the specific user account running the Kedro service. The detection logic should flag any file access that results in a path outside the designated data directory, particularly if the accessed file contains sensitive data such as credentials, configuration files, or system binaries. Additionally, monitoring for unexpected process creation or script execution following file access can help identify post-exploitation activities. It is important to note that this detection approach may generate false positives if legitimate operations involve accessing files outside the standard directory, so baseline behavior must be established before deploying alerts.

Validation of the detection logic requires a controlled test environment where the vulnerable version of kedro-datasets is installed and a benign path traversal payload is executed. The test should verify that the detection rule triggers when the MatplotlibDataset component attempts to access a file outside the intended directory. Required telemetry includes file system access logs, process execution logs, and network connection logs if the vulnerability is exploited to exfiltrate data. The telemetry must capture the full file path, the user account performing the access, and the timestamp of the event. Without this level of detail, it is impossible to distinguish between legitimate file access and malicious exploitation. The validation process should also include a review of the application's error logs to ensure that path traversal attempts are logged and do not result in unhandled exceptions that could crash the service. If the telemetry is insufficient, the detection rule should be withheld from production deployment until the necessary logging capabilities are implemented. This ensures that the detection logic is both accurate and actionable, reducing the risk of alert fatigue and missed compromises.

Threat hunting should focus on identifying historical instances of path traversal attempts in the application logs. The following queries can be used to search for potential exploitation:

• Search for file access events where the path contains "../" or "..\\" sequences.
• Identify file access events by the Kedro service account that target files outside the designated data directory.
• Review network connection logs for unexpected outbound connections from the Kedro service, which may indicate data exfiltration.
• Analyze process creation logs for the execution of shell commands or scripts by the Kedro service, which could indicate post-exploitation activity.
• Correlate file access events with authentication logs to identify any unauthorized access to sensitive files.
• Search for error messages in the application logs that indicate path resolution failures or permission denied errors.
• Review dependency management logs to identify when the vulnerable version of kedro-datasets was installed and updated.
• Examine backup logs to ensure that no unauthorized changes have been made to critical configuration files or data artifacts.

These queries should be executed against the organization's SIEM or log management platform to identify any past or ongoing exploitation of the vulnerability. The results should be reviewed by a security analyst to determine the scope of the compromise and the appropriate response actions. If no evidence of exploitation is found, the organization can proceed with remediation without immediate containment measures. However, if evidence of exploitation is identified, the incident response team should be engaged to contain the threat and assess the impact.

Report-Type Deep Dive

This CVE Vulnerability Advisory record is analyzed according to the evidence available for its family. Unsupported exploit chains, malware capabilities, breach scope, attribution, encryption, exfiltration, and regulatory consequences are intentionally not synthesized.

MITRE ATT&CK Assessment

Not established in cited evidence. ATT&CK technique mapping requires source-backed behavior. Any mappings present in the preserved analytical body are subject to the existing ReportX semantic gate; the compiler adds none of its own. The factory does not infer the missing fact from absence of evidence.

Indicators & Observables

VERIFIED ARTIFACTS AVAILABLE. Indicators extracted directly from the sourced article are published below in the Indicators / Observables section (defanged for safe display).

Detection Engineering Guidance

Detection content remains evidence-conditioned. Production rules require source-backed behavior plus the telemetry needed to validate precision; otherwise the correct output is a telemetry specification rather than fabricated executable logic.

Detection Validation & Required Telemetry

• Identify the telemetry source and fields needed to observe the cited behavior before promoting detection logic.
• Replay or safely simulate only evidence-supported behavior, measure expected false positives, and retain test evidence with the rule version.
• Do not represent syntax validation as production efficacy; deployment requires environment-specific validation and accountable approval.

Threat Hunting Queries

Threat hunting should begin with exposure to the technology or service named in the cited record and then pivot only on observables or behavior established in cited evidence. Absence of a source-backed pivot is an intelligence gap, not permission to invent one.

SOC Analyst Playbook

• Validate whether the affected technology, identity, dependency, service, or victim context exists internally.
• Preserve relevant logs and timestamps before destructive containment when an internal match is found.
• Escalate from intelligence validation to incident response only when internal telemetry or independently cited evidence supports the transition.

Incident Response & Containment Decision Plan

Public threat intelligence alone does not establish an internal incident. If internal evidence confirms relevance, preserve evidence, scope affected assets and identities, contain the validated path, eradicate the supported cause, recover, and verify recurrence controls.

Remediation & Validation Plan

• Remediation state from the normalized record: Availability not established in the source record — verify vendor guidance.
• Apply vendor/CISA remediation only when that action is actually present in trusted evidence; otherwise obtain the authoritative remediation source before changing production systems.
• After change, verify asset/version state, exposure path, relevant telemetry, and rollback readiness rather than assuming the change succeeded.

Executive Decision Matrix

• No internal exposure evidence: retain as intelligence, document the negative exposure basis, and continue collection.
• Exposure confirmed but compromise unconfirmed: prioritize supported remediation and heightened telemetry while avoiding unsupported incident claims.
• Internal malicious activity confirmed: activate the organization's incident-response process using the preserved evidence and scoped telemetry.

Executive Recommendations

• Validate enterprise relevance to the technology or service named in the cited record before treating the public record as a customer-specific finding.
• Prioritize evidence-backed remediation, telemetry coverage, and verification over severity labels alone.
• Keep unresolved exploitation, attribution, breach scope, and impact statements explicitly unresolved until corroborating evidence exists.

Intelligence Gaps & Collection Requirements

• Customer-specific exposure is not established by the public source record.
• Customer-specific compromise or impact is not established unless internal telemetry is supplied.
• Any source dimension not represented in the normalized evidence graph requires additional authoritative collection before it can drive a material claim.

Analytic Confidence & Limitations

Confidence is bounded by source reliability, information credibility, corroboration state, and unresolved contradictions already computed by ReportX. Structural compilation never raises epistemic confidence.

Forecast & Outlook

No new future event is predicted by the compiler. Forecast statements, when present in the preserved analytical body, must remain tied to ReportX supporting observation claims and explicit confidence rationale.

References

• CYBERDUDEBIVASH — https://blog.cyberdudebivash.in/posts/cve-2026-111170.html