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CVE-2026-2858 is a heap-buffer-overflow (out-of-bounds read) vulnerability in the Wren scripting language affecting all versions up to and including 0.4.0. The flaw resides in the peekChar function within src/vm/wren_compiler.c, triggered during source file parsing of malformed raw string literals. It was reported via GitHub issue #1217 on January 22, 2026, and publicly disclosed on February 20, 2026; the project maintainers had not responded as of the disclosure date. It carries a CVSS v3.1 base score of 7.1 (High) (Red Hat CVE, Wren Issue #1217).
The root cause is classified as CWE-125 (Out-of-bounds Read) and CWE-119 (Improper Restriction of Operations within the Bounds of a Memory Buffer). The vulnerability occurs in peekChar (line 641 of wren_compiler.c), called by readRawString, which is invoked by nextToken when the lexer encounters a triple-quote (""") sequence. When parsing a malformed or incomplete raw string literal containing an excessive sequence of quote characters, the loop logic in readRawString fails to correctly detect the end of the heap-allocated source buffer (EOF/null terminator), causing a one-byte read past the end of the allocated region. Exploitation requires local access and the ability to supply a crafted Wren source file to the interpreter; a public proof-of-concept reproducer is available (Wren Issue #1217, PoC Repro).
Successful exploitation can result in a crash of the Wren VM (denial of service) due to the out-of-bounds read, and may expose one byte of heap memory adjacent to the source buffer, posing a limited confidentiality risk. The integrity of the system is not directly affected. Because Wren is designed as an embeddable scripting language, applications that allow untrusted users to supply Wren source code for interpretation are most at risk, as a crafted script could crash the host application or potentially leak sensitive heap data (Wren Issue #1217, Red Hat CVE).
A public proof-of-concept reproducer is available in the GitHub issue report and a linked repository, demonstrating the crash under AddressSanitizer. The EPSS score is 0.013% (very low probability of widespread exploitation). There is no evidence of in-the-wild exploitation, no known threat actor attribution, and the vulnerability is not listed in the CISA Known Exploited Vulnerabilities catalog. The CVSS v4.0 exploit maturity is rated as "Proof of Concept" (Feedly, Wren Issue #1217).
System.prim.print("Hello")nt(""""""""" (as demonstrated in the public PoC)../wren_vm malicious.wren or via the wrenInterpret() API.nextToken function calls readRawString, which invokes peekChar at wren_compiler.c:641. The loop fails to detect EOF when processing the excessive quotes, reading one byte past the end of the heap-allocated source buffer.peekChar at wren_compiler.c:641 or readRawString in crash logs; error messages such as heap-buffer-overflow from ASan-instrumented builds..wren source files containing long sequences of consecutive quote characters (e.g., """"""""") in directories used for script input.No official patch has been released by the wren-lang project as of the disclosure date, and the maintainers had not responded to the issue report. As a workaround, applications embedding Wren should avoid interpreting untrusted or user-supplied source files until a fix is available. Developers can apply input validation to reject source files containing malformed or excessive quote sequences before passing them to wrenInterpret(). Monitoring the wren-lang/wren GitHub repository for a patch addressing issue #1217 is recommended (Wren Issue #1217, Red Hat CVE).
Red Hat has tracked this vulnerability in their security advisory database, though no Red Hat products are known to be directly affected. The vulnerability was reported through the standard GitHub issue tracker, and the wren-lang project had not responded as of the disclosure date, which was noted in the CVE description. Coverage has been limited to vulnerability aggregator sites such as VulDB, Vulners, and INCIBE-CERT (Red Hat CVE, Wren Issue #1217).
Source: This report was generated using AI
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