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CVE-2023-7101

Spreadsheet::ParseExcel version 0.65 is a Perl module used for parsing Excel files.

High Known exploited
Severity
7.8 / 10
High · CVSS 3.1
Exploit chance (EPSS)
18.96%
Chance of exploitation in the next 30 days. Higher than 97.30% of scored vulnerabilities.
Known exploited
Yes
In CISA's catalog since Jan 2, 2024
Weakness
CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
None yet
No fixed version published
Exploited in the wild. Spreadsheet::ParseExcel Remote Code Execution Vulnerability (Spreadsheet::ParseExcel Spreadsheet::ParseExcel). Required action: Apply mitigations per vendor instructions or discontinue use of the product if mitigations are unavailable. Due for U.S. federal agencies by Jan 23, 2024.

Description

Spreadsheet::ParseExcel version 0.65 is a Perl module used for parsing Excel files. Spreadsheet::ParseExcel is vulnerable to an arbitrary code execution (ACE) vulnerability due to passing unvalidated input from a file into a string-type “eval”. Specifically, the issue stems from the evaluation of Number format strings (not to be confused with printf-style format strings) within the Excel parsing logic.

How to fix

No fixed version has been published yet. Until one is, remove or replace the package, or turn off or isolate the affected feature. This page updates when a fix ships.

Severity breakdown

CVSS 3.1 7.8

CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Attack vector
Local
Attack complexity
Low
Privileges required
None
User interaction
Required
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Weakness

CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')

The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. "eval").

How to prevent it
  • Architecture and Design, Implementation: If possible, refactor your code so that it does not need to use eval() at all.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
  • Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it
  • Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
  • Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
  • Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
  • Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Advisories

Fixes and patches

Other links

Timeline

  1. Published
  2. Added to CISA's Known Exploited Vulnerabilities catalog
  3. Remediation due for U.S. federal agencies
  4. Last updated by the source
  5. Latest EPSS score
AdvisorySeverityWhy it's related
PYSEC-2026-3727
openssl_encrypt versions before 1.4.0 contain a sandbox escape vulnerability in IsolatedPluginExecutor that exposes Python type objects in restricted exec()…
Critical Same weakness, CWE-95
GHSA-4j8x-x6v7-w9rq
Flowise: RCE via CSVAgent csvFile data URI base64 segment is interpolated into Python source without validation
Critical Same weakness, CWE-95
GHSA-wg86-r78f-74mp
Flowise Sandbox Escape to RCE
High Same weakness, CWE-95
GHSA-848c-c2cx-j7qx
NLTK vulnerable to Eval Injection via collocations CLI arguments
High Same weakness, CWE-95
GHSA-v84x-qvhg-f36r
MantisBT: Remote Code Execution via eval() Class Hoisting in adm_config_set.php
High Same weakness, CWE-95
GHSA-79fv-7hq9-w7xg
PraisonAI: API deploy code generator embeds unescaped YAML fields into Python source
High Same weakness, CWE-95
GHSA-8qpj-27x8-pwpq
Langflow: PythonREPLComponent executes unsandboxed Python code, enabling authenticated RCE and privilege escalation
Critical Same weakness, CWE-95
GHSA-mhc6-2gfq-xx62
Rancher vulnerable to command injection through unsanitized YAML parameter
Critical Same weakness, CWE-95
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