Unsafe Code Generation in ROOT File Parsing

uproot dynamically generates Python class source code from ROOT TStreamerInfo records in a file and compiles it at runtime. Some file-controlled streamer metadata fields (for example, streamer element names) are interpolated into the generated Python source without safe quoting via repr() or the !r format specifier. An attacker who can supply a crafted ROOT file can place Python expression-breaking content into a streamer metadata field. When uproot generates and invokes the corresponding reader method, the injected Python expression is evaluated in the context of the process opening the file, resulting in arbitrary Python code execution in applications that open or process attacker-controlled ROOT files with affected uproot code paths.

Casky was already ahead

This CVE exploits attack patterns that Casky's 322matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.

Analysis

CVE-2026-9147 is a code injection vulnerability in the uproot library, which parses ROOT data files commonly used in high-energy physics research. The vulnerability arises from unsafe dynamic Python code generation—uproot constructs Python class definitions at runtime based on metadata embedded in ROOT files, but fails to properly sanitize streamer metadata fields before inserting them into source code. An attacker crafting a malicious ROOT file can inject Python expressions into fields like streamer element names, breaking out of string contexts and executing arbitrary code when uproot processes the file. This affects any organization or researcher using uproot to analyze ROOT files from untrusted sources, including academic institutions, research labs, and cloud-based data analysis platforms.

Casky's 322 matching skills leverage Claude's reasoning to detect the attack patterns mapped to TA0001 (Reconnaissance) and TA0043 (Resource Development). Practitioners using Casky would see detections around dynamic code generation risks, unsafe string interpolation patterns, and metadata-driven code synthesis—the core mechanisms enabling code injection here. The platform's skills would flag the absence of proper input validation (repr() quoting) and identify how crafted ROOT files can be weaponized as delivery vehicles. Practitioners would observe findings related to untrusted deserialization, runtime code compilation from external sources, and exploitation chains where file parsing directly precedes code execution—key indicators that distinguish this vulnerability from standard parsing flaws and surface the adversarial intent required to exploit it.

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Casky Risk Score

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EPSS Probability

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Shodan Exposure

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GreyNoise Signal

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Composite risk scoring from EPSS, CISA KEV, Shodan, and GreyNoise — 21 security APIs correlated into a single Casky Risk Score. Coming in Casky Pro. Join early access →

322 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-9147.

Abuse of Public-Facing API: Mobile API Abuse

mobile security · low

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abusing-dpapi-for-credential-access

red teaming · high

Run

abusing-shadow-credentials-for-privesc

red teaming · high

Run

MITRE ATT&CK Techniques

TA0001 TA0043

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