Skip to main content
CVE Vulnerability Database
Vulnerability Database/CVE-2025-69647

CVE-2025-69647: GNU Binutils readelf DoS Vulnerability

CVE-2025-69647 is a denial-of-service flaw in GNU Binutils readelf that causes infinite loops when processing malformed DWARF data. This article covers the technical details, affected versions, impact, and mitigation.

Published:

CVE-2025-69647 Overview

GNU Binutils through version 2.45.1 contains a denial-of-service vulnerability in the readelf utility when processing crafted binaries with malformed DWARF loclists data. A logic flaw in the DWARF parsing code can cause readelf to repeatedly print the same table output without making forward progress, resulting in an unbounded output loop that never terminates unless externally interrupted. A local attacker can trigger this behavior by supplying a malicious input file, causing excessive CPU and I/O usage and preventing readelf from completing its analysis.

Critical Impact

Attackers can craft malicious binary files that cause readelf to enter an infinite loop, consuming system resources and preventing legitimate analysis of ELF binaries. This vulnerability is classified as CWE-835 (Loop with Unreachable Exit Condition).

Affected Products

  • GNU Binutils through version 2.45.1
  • readelf utility with DWARF parsing functionality
  • Systems utilizing automated binary analysis pipelines

Discovery Timeline

  • 2026-03-09 - CVE-2025-69647 published to NVD
  • 2026-03-11 - Last updated in NVD database

Technical Details for CVE-2025-69647

Vulnerability Analysis

This vulnerability manifests as an infinite loop condition within the DWARF loclists parsing functionality of GNU Binutils' readelf utility. When processing specially crafted ELF binaries containing malformed DWARF debugging information, the parser fails to properly validate loop termination conditions. The DWARF loclists data structure is used to describe the location of variables in debugged programs, and the vulnerability occurs when the parsing logic encounters manipulated offset values or corrupted list terminators.

The attack requires local access, as the attacker must provide a malicious binary file as input to readelf. No user interaction is required beyond invoking readelf on the crafted file. While no code execution occurs, the availability impact is significant—affected systems can experience CPU exhaustion and excessive I/O activity until the process is manually terminated.

Root Cause

The root cause is a logic flaw (CWE-835: Loop with Unreachable Exit Condition) in the DWARF loclists parsing code. The vulnerable code path fails to properly detect when the parser is no longer making forward progress through the input data, resulting in repeated processing of the same data elements without advancing to a termination state.

Attack Vector

The attack vector is local, requiring the attacker to supply a crafted binary file containing malformed DWARF loclists data to the readelf utility. This could be exploited in scenarios where automated build systems, security scanners, or binary analysis tools process untrusted ELF files using vulnerable versions of Binutils.

The vulnerability mechanism involves crafted DWARF debugging sections that cause the loclists parser to repeatedly output the same table data. Technical details of the specific malformed data structures that trigger this behavior can be found in the Sourceware Bug Report #33640.

Detection Methods for CVE-2025-69647

Indicators of Compromise

  • Abnormally long-running readelf processes that do not complete
  • Excessive CPU utilization by readelf processes
  • Unusually large output files generated by readelf operations
  • System resource exhaustion on build servers or binary analysis pipelines

Detection Strategies

  • Monitor for readelf processes with extended execution times exceeding normal thresholds
  • Implement resource limits (CPU time, memory, output size) for automated binary analysis tools
  • Deploy process monitoring to detect runaway readelf instances
  • Use checksums or file validation on input binaries before processing

Monitoring Recommendations

  • Configure process accounting to track readelf execution duration and resource consumption
  • Set up alerts for processes consuming excessive CPU without producing expected output
  • Monitor disk space usage for unexpected growth in tool output directories
  • Implement timeout mechanisms for batch processing of binary files

How to Mitigate CVE-2025-69647

Immediate Actions Required

  • Update GNU Binutils to a patched version containing the fix referenced in commit 455446bbdc8675f34808187de2bbad4682016ff7
  • Implement process timeouts when running readelf against untrusted input files
  • Restrict access to binary analysis tools to trusted users only
  • Validate input files before processing with readelf

Patch Information

A fix for this vulnerability has been committed to the Binutils Git repository. The patch is available at Sourceware Git Commit #455446. Organizations should apply this patch or upgrade to a Binutils version that includes this fix. For detailed bug tracking information, see Sourceware Bug Report #33640.

Workarounds

  • Run readelf with system-level resource limits using ulimit or cgroups
  • Implement timeout wrappers around readelf invocations in automated pipelines
  • Isolate binary analysis operations in sandboxed environments with resource constraints
  • Pre-validate ELF files using lightweight parsers before full analysis
bash
# Configuration example - Apply resource limits when running readelf
# Set CPU time limit to 60 seconds
ulimit -t 60

# Run readelf with timeout wrapper
timeout 60 readelf -w suspicious_binary.elf

# Alternative: Use cgroups for more granular control
# Create a cgroup with CPU and memory limits for binary analysis
cgcreate -g cpu,memory:/binutils_sandbox
echo 100000 > /sys/fs/cgroup/cpu/binutils_sandbox/cpu.cfs_quota_us
echo 536870912 > /sys/fs/cgroup/memory/binutils_sandbox/memory.limit_in_bytes
cgexec -g cpu,memory:/binutils_sandbox readelf -w suspicious_binary.elf

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

Default Legacy - Prefooter | Experience the World’s Most Advanced Cybersecurity Platform

Experience the Most Advanced Cybersecurity Platform

See how the world’s most intelligent, autonomous cybersecurity platform can protect your organization today and into the future.