Breach Response Analysis

5 automated security scanners

Disclosure Timing Anomalies

Purpose: This scanner identifies potential disclosure timing anomalies for a given domain and company by analyzing the dates and contexts of disclosed information. It checks for anomalies such as weekend releases, Friday evening disclosures, holiday proximity disclosures, and delayed disclosures that may indicate strategic timing to minimize media attention or bury negative news.

What It Detects:

Weekend Release Detection: Disclosures released on weekends might receive less media coverage due to reduced public awareness periods.
Friday Evening Disclosure: Some organizations release bad news late in the day, often on a Friday, to minimize its impact and avoid immediate public scrutiny.
Holiday Proximity Disclosure: Releases around holidays may be overlooked or delayed due to holiday distractions, potentially reducing media attention.
Delayed Disclosure Indicators: Delays in disclosing information can sometimes indicate attempts to manage the narrative or hide negative events.

Inputs Required:

<domain>: The target website’s domain for analysis.
<company_name>: The name of the company associated with the domain, used for context and reporting.

Business Impact: This analysis is crucial as it helps in assessing the timeliness and transparency of breach responses. Incorrect or strategic disclosure timing can significantly impact a company’s reputation and regulatory compliance. It also aids in detecting potential PR strategies that might be masking critical issues.

Risk Levels:

Critical: If multiple critical anomalies are detected, such as widespread use of strategic disclosure tactics across various incidents, it could indicate severe vulnerabilities in the organization’s crisis management practices.
High: Detection of weekend releases or Friday evening disclosures without a clear strategic reason might suggest attempts to bury negative news.
Medium: A single anomaly or limited instances of strategic timing might lead to medium risk findings depending on other contextual factors and potential impacts.
Low: Minimal anomalies generally indicate standard disclosure practices, leading to low risk levels unless accompanied by other indicators suggesting broader issues.
Info: Informal detection of minor discrepancies that do not significantly impact the overall risk assessment but can be part of a continuous improvement process in disclosure strategies.

If specific risk thresholds are not defined in the README, they have been inferred based on the scanner’s purpose and potential impacts.

Example Findings:

A company consistently releases critical information around holidays to avoid public scrutiny, indicating strategic timing that could be interpreted as evasive behavior.
A late-Friday disclosure of significant negative events might suggest a deliberate attempt to manage press attention downward before the weekend begins.

Technical Detail Suppression

Purpose: This scanner analyzes technical detail suppression in breach response reports for cybersecurity assessments. It identifies vague attack vectors, undefined scope, lack of specific dates and methods, unacknowledged control failures, and superficial remediation claims to assess the completeness and accuracy of disclosure statements.

What It Detects:

Attack Vector Suppression: The scanner detects when an unauthorized access is vaguely mentioned without specifying details like method or type.
Scope Suppression: It identifies vague numerical limitations on scope that do not provide clear context for what is considered limited, potentially hiding larger issues.
Timeline Suppression: This involves the absence of precise dates or times in breach response timelines which can obfuscate critical timing information.
Root Cause Suppression: The scanner flags situations where no explicit acknowledgment of systemic flaws or control failures is made, which might indicate a lack of transparency and understanding of the incident’s origins.
Remediation Suppression: It detects when remediation strategies are only vaguely outlined without concrete actions to address the issues identified during an investigation.

Inputs Required:

<domain>: The target domain for assessment, representing the online presence or platform under scrutiny.
<company_name>: The name of the company associated with the domain, which helps contextualize breach response efforts and disclosures within a specific organizational framework.

Business Impact: Technical detail suppression in breach response reports can significantly impact cybersecurity posture by obscuring critical information that stakeholders rely on for informed decision-making. Incomplete or inaccurate disclosure can lead to ineffective risk mitigation strategies and potentially prolong the exposure of vulnerabilities, affecting both operational efficiency and public trust.

Risk Levels:

Critical: When multiple suppression points are identified in a single report, indicating systemic issues with breach response transparency and integrity.
High: When significant suppression is detected, suggesting a lack of detailed investigation or incomplete understanding of the incident by the organization.
Medium: When moderate levels of suppression are present but do not reach critical mass, indicating potential gaps in disclosure that could be improved for clearer risk communication.
Low: When no or minimal suppression points are identified, reflecting robust and transparent breach response practices.
Info: When minor issues with specific detection points are found but do not compromise overall transparency or integrity of the report.

If the README doesn’t specify exact risk levels, infer them based on the scanner’s purpose and impact.

Example Findings:

A breach response document for a financial institution mentions “unauthorized access” without specifying whether it was via phishing emails or direct database entry. This could be indicative of suppression in attack vector detail.
An incident report does not define the scope clearly, using vague language that might suggest broader impacts than initially understood by the organization.

Asset Renaming

Purpose: The Asset Renaming Scanner is designed to analyze infrastructure naming patterns before and after security incidents in order to detect asset renaming used to obscure compromised systems, hide breach scope, or evade regulatory scrutiny. It compares DNS records, certificate transparency logs, subdomain enumeration, and cloud resource naming across time periods to identify any suspicious changes.

What It Detects:

DNS Record Changes: The scanner tests for modifications in A/AAAA record hostnames, checks for subdomain creation/deletion patterns, verifies CNAME target changes, detects alterations in MX records, and flags suspicious timing of DNS changes.
Certificate Transparency Analysis: It tests for certificate reissuance with different CNs, checks for SAN list modifications, verifies wildcard certificate replacements, detects certificate revocation followed by new issuance, and flags certificates issued immediately after incidents.
Subdomain Enumeration Comparison: The scanner identifies disappeared subdomains and newly appeared subdomains with similar functions, verifies naming pattern shifts, detects wholesale subdomain namespace changes, and flags timing correlation with public incidents.
Cloud Resource Naming: It tests for S3 bucket name changes, checks for Azure storage account renaming, verifies GCP project/resource naming shifts, detects EC2 instance tag modifications, and flags resource naming pattern breaks.

Inputs Required:

domain (string): The primary domain to analyze (e.g., acme.com)
company_name (string): The company name for context correlation (e.g., “Acme Corporation”)

Business Impact: Asset renaming after breaches signals deliberate obfuscation, which can lead to unauthorized access and data theft. This affects the security posture by making it difficult to track compromised assets and respond effectively to incidents.

Risk Levels:

Critical: The scanner must pass validation with two arguments: domain and company_name, return valid JSON with all required fields, and execute without errors on a test domain like google.com “Google”.
High: Conditions for high severity are not explicitly defined in the README; however, it is implied that severe changes in asset naming patterns would be indicative of significant security vulnerabilities or breaches.
Medium: Conditions for medium severity involve more nuanced deviations from normal naming practices but still within recognizable cybersecurity indicators.
Low: Informational findings may include minor variations in naming conventions that do not pose immediate risks but could indicate ongoing issues needing investigation.

If the README doesn’t specify exact risk levels, infer them based on the scanner’s purpose and impact.

Example Findings: The scanner might flag generic naming patterns like “temp1” or “backup2” as suspicious, especially when detected in domains associated with significant security incidents. It could also identify rapid certificate issuance following a breach as indicative of ongoing attempts to obscure activity.

Transparency Pattern Shifts

Purpose: This scanner analyzes transparency pattern shifts for a given domain and company to assess their breach response and communication strategies. It evaluates the presence of active communication channels, recent posts, removed security pages, blocked security pages, gaps in transparency reports, restricted access on certain pages, and the existence of bug bounty or vulnerability disclosure programs.

What It Detects:

Communication Channels: Identifies whether blog, newsroom, security page, status page, and advisory page are active.
Recent Posts: Checks the frequency of posts to gauge transparency and communication efforts.
Removed Security Pages: Indicates if certain expected pages return a 404 error code.
Blocked Security Pages: Detects if security pages are blocked in robots.txt.
Transparency Report Gaps: Uncovers any missing years or complete gaps in the transparency report history.
Access Restrictions: Monitors restrictions on certain security pages that should be accessible to users.
Bug Bounty and Vulnerability Disclosure Programs: Evaluates the presence of active programs for responsible disclosure of vulnerabilities.

Inputs Required:

<domain>: The target domain name (e.g., “acme.com”).
<company_name>: The official name or identifier of the company associated with the domain.

Business Impact: This analysis is crucial for understanding how effectively a company communicates its security posture, breach response plans, and transparency efforts to stakeholders and the public. Poor communication can lead to increased risk perception and hinder trust-building initiatives.

Risk Levels:

Critical: The scanner flags multiple critical issues such as no active channels, missing years in reports, and complete gaps without a clear mitigation plan.
High: Significant deficiencies like low posting frequency, removed security pages, and blocked pages indicate high risk of information leakage or lack of proactive communication strategies.
Medium: Some limitations in transparency efforts, restricted access to certain pages suggest moderate vulnerability that could be improved with better practices.
Low: Minimal issues point towards a generally healthy breach response and communication strategy without significant concerns.
Info: Informal findings such as outdated information or minor discrepancies are considered informational but still need attention for continuous improvement.

Example Findings:

“Limited communication channels: only 2 active channels” - Indicates that the company’s primary means of public interaction is severely limited, potentially affecting transparency and trust.
“Removed security pages: 3 expected pages return 404” - Signifies a significant loss of critical information on the company’s website, which could be exploited by malicious actors.

Blame Deflection Patterns

Purpose: This scanner analyzes blame deflection patterns in breach response analysis reports. It identifies if a company has attempted to shift responsibility by attributing breaches to nation-state actors, third parties, or blaming individual employees without providing technical evidence. The goal is to assess the risk of such deflections and their potential impact on security posture.

What It Detects:

Attribution Deflection: When a breach response report attributes the incident to a nation-state actor or APT group without providing detailed technical information about how this was achieved.
Third-Party Blame: When blame is shifted to third parties (vendors, contractors) without addressing systemic issues within the company.
Employee Scapegoating: When individual employees are scapegoated as responsible for a breach, avoiding accountability for broader system or procedural failures.
Passive Voice Overuse: Excessive use of passive voice in reports that avoids holding any party accountable for the incident.

Inputs Required:

Domain: The internet domain name under investigation (e.g., “acme.com”).
Company Name: The official name or identifier of the company (e.g., “Acme Corporation”).

Business Impact: Shifting blame in breach response reports can mislead stakeholders and potentially delay necessary security improvements. It can also undermine trust in the organization’s ability to handle cybersecurity incidents effectively. Accurate attribution and responsibility allocation are crucial for enhancing security measures and preventing future breaches.

Risk Levels:

Critical: When multiple deflections (attribution, third-party blame, employee scapegoating) are detected without any technical evidence or when the passive voice ratio is over 50%.
High: When there is a single significant deflection that lacks supporting technical details.
Medium: When moderate levels of deflection are present but mitigated by sufficient technical information and clear accountability statements.
Low: When no deflections are detected, or when the passive voice ratio is within an acceptable range for cybersecurity reporting standards.
Info: When minor instances of passive voice usage do not significantly impact the overall risk assessment.

Example Findings:

“Acme Corporation’s breach response report attributes a sophisticated nation-state actor as the perpetrator without providing technical details about how this was accomplished.”
“The company shifted blame to third-party vendors by name, but failed to address internal systemic issues that could have contributed to the breach.”