CVE-2025-0693: AWS IAM Authentication Bypass Vulnerability

CVE-2025-0693 Overview

CVE-2025-0693 is a timing attack vulnerability in the AWS Sign-in IAM user login flow that enables attackers to enumerate valid IAM usernames in arbitrary AWS accounts. The vulnerability stems from variable response times during the authentication process, which can be exploited through brute force enumeration techniques to determine whether a specific IAM username exists within a target AWS account.

This vulnerability is classified under CWE-204 (Observable Response Discrepancy), where differences in application behavior can reveal sensitive information to attackers.

Critical Impact

Attackers can enumerate valid IAM usernames in any AWS account without authentication, enabling targeted credential stuffing attacks and social engineering campaigns against identified users.

Affected Products

• AWS Sign-in IAM user login flow
• AWS Identity and Access Management (IAM) authentication endpoints

Discovery Timeline

• 2025-01-23 - CVE CVE-2025-0693 published to NVD
• 2025-10-14 - Last updated in NVD database

Technical Details for CVE-2025-0693

Vulnerability Analysis

The vulnerability exploits a timing side-channel in the AWS Sign-in authentication mechanism. When a user attempts to authenticate with an IAM username, the system exhibits measurably different response times depending on whether the supplied username exists in the target AWS account. This timing discrepancy allows attackers to statistically distinguish between valid and invalid usernames by analyzing response latencies across multiple requests.

The attack is network-accessible, requires no authentication or user interaction, and can be executed with low complexity. While the vulnerability does not directly compromise credentials or grant unauthorized access, the information disclosure enables attackers to build accurate lists of valid IAM users for subsequent targeted attacks.

Root Cause

The root cause is classified as CWE-204 (Observable Response Discrepancy). The AWS authentication backend performs different code paths or database lookups depending on whether a username exists, resulting in observable timing differences. This information leakage occurs because the system does not implement constant-time username validation, allowing attackers to infer the existence of usernames through careful measurement of response timing patterns.

Attack Vector

The attack is conducted over the network against the AWS Sign-in endpoint. An attacker can submit authentication requests with candidate usernames and measure the response times. By collecting timing data across many requests and applying statistical analysis, the attacker can reliably determine which usernames are valid within a target AWS account. This technique requires no prior authentication, can target any AWS account ID, and can be automated to enumerate large numbers of potential usernames.

The attack flow involves:

1. Identifying the target AWS account ID
2. Generating a list of candidate IAM usernames based on common naming conventions or gathered intelligence
3. Submitting login requests for each candidate username while measuring response latency
4. Applying statistical analysis to identify usernames with distinctive timing signatures
5. Building a list of confirmed valid usernames for subsequent attacks

Detection Methods for CVE-2025-0693

Indicators of Compromise

• Unusually high volumes of failed authentication attempts against the AWS Sign-in page from single IP addresses or IP ranges
• Rapid sequential login attempts testing different IAM usernames for the same AWS account
• Login attempt patterns that systematically test common username formats or dictionary-based username lists
• Authentication requests exhibiting characteristics of automated tooling (consistent timing, missing browser fingerprints)

Detection Strategies

• Monitor AWS CloudTrail for ConsoleLogin events with high failure rates originating from suspicious sources
• Implement anomaly detection on authentication request patterns to identify enumeration behavior
• Configure AWS CloudWatch alarms for authentication rate thresholds that indicate brute force activity
• Analyze authentication logs for sequential requests testing different usernames in alphabetical or pattern-based order

Monitoring Recommendations

• Enable detailed CloudTrail logging for all IAM authentication events across all regions
• Implement real-time alerting on authentication anomalies using AWS Security Hub or third-party SIEM solutions
• Review AWS GuardDuty findings for unusual API activity related to authentication endpoints
• Establish baseline authentication patterns to more effectively identify enumeration attempts

How to Mitigate CVE-2025-0693

Immediate Actions Required

• Review the AWS Security Bulletin AWS-2025-002 for official guidance and remediation steps
• Enable AWS CloudTrail logging if not already active to maintain visibility into authentication events
• Consider implementing AWS Organizations Service Control Policies (SCPs) to enforce additional authentication controls
• Audit IAM usernames to ensure they do not follow easily guessable patterns

Patch Information

AWS has addressed this vulnerability on the backend infrastructure. According to the AWS Security Bulletin AWS-2025-002, no customer action is required to receive the fix as AWS has implemented server-side mitigations to normalize response times during the authentication flow.

Workarounds

• Implement AWS IAM Identity Center (formerly AWS SSO) for centralized identity management with additional security controls
• Enable MFA for all IAM users to add a second authentication factor regardless of username enumeration risk
• Use complex, non-guessable IAM username formats that are resistant to dictionary-based enumeration
• Consider implementing AWS WAF rules to rate-limit authentication requests from suspicious sources
• Monitor for and respond to enumeration attempts using security information and event management (SIEM) solutions