Bounty Program Design

5 automated security scanners

Scope Limitation Analysis

Purpose: The Scope Limitation Analysis Scanner is designed to identify excessive exclusions, critical asset omissions, and core technology exemptions in a company’s security posture by analyzing publicly available data sources. This tool helps ensure that the scope of security assessments is comprehensive and does not inadvertently overlook key areas.

What It Detects:

Excessive Exclusions: The scanner identifies patterns indicating broad exclusions from security scopes, such as “all test environments” or “non-production systems.”
Critical Asset Omissions: It detects mentions of critical assets that are not included in security assessments, including financial databases and customer data.
Core Technology Exemptions: The scanner can detect statements indicating exemptions for core technologies or platforms used by the company, such as AWS, Azure, or Kubernetes.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: Identifying excessive exclusions, critical asset omissions, and core technology exemptions is crucial as it ensures that security assessments are not only comprehensive but also focused on areas of highest risk to the organization. This can help in prioritizing resources effectively and improving overall security posture.

Risk Levels:

Critical: The scanner should flag conditions where there are broad exclusions from security scopes or significant omissions of critical assets, which could lead to severe vulnerabilities if not addressed.
High: Conditions that indicate important systems or data are not covered by current security policies or controls warrant high attention due to potential exposure to risks.
Medium: Medium severity findings involve areas where there might be some gaps in coverage but still pose significant risk if left unchecked.
Low: Informational findings, while useful for awareness and continuous improvement, generally do not carry a direct threat unless amplified by other factors.

Example Findings:

A company may have excluded “all test environments” from their security scope, which could lead to overlooking potential vulnerabilities in these areas.
A notable omission of “financial databases” might indicate a risk of unauthorized access or data breaches that are not currently being monitored for.

Researcher Relationship Management

Purpose: The Researcher Relationship Management Scanner evaluates communication quality, response timeliness, and researcher satisfaction by analyzing public records and open-source intelligence (OSINT) sources. This tool helps identify potential issues in how a company interacts with security researchers, which can impact the overall security posture and trust.

What It Detects:

Communication Quality: Identifies vague or non-specific communication regarding security vulnerabilities through patterns like “data breach,” “security incident,” “unauthorized access,” and “compromised.”
Response Timeliness: Looks for mentions of quick or delayed responses to security issues, with patterns such as “responded within [number] hours” and “delay in response.”
Researcher Acknowledgment: Checks for acknowledgment of researchers’ contributions through phrases like “thank you to [Name]” and mentions of bug bounty programs.
Technical Details Disclosure: Identifies the level of technical detail provided in responses, with patterns such as “technical details” and “root cause analysis.”
Resolution Status: Evaluates whether vulnerabilities are acknowledged as resolved or ongoing, with phrases like “resolved vulnerability,” “ongoing investigation,” and “patch released.”

Inputs Required:

Domain (string): The primary domain to analyze, such as acme.com.
Company Name (string): The company name for statement searching, such as “Acme Corporation.”

Business Impact: This scanner is crucial for maintaining a healthy relationship with security researchers, which directly impacts the company’s vulnerability disclosure practices and overall cybersecurity posture. Effective communication and timely responses can significantly enhance trust and reduce vulnerabilities that could be exploited by malicious actors.

Risk Levels:

Critical: Conditions where there are vague or non-specific communications about security issues, significant delays in response to reported vulnerabilities, or lack of acknowledgment for researchers’ contributions.
High: Conditions where responses provide minimal technical details, ongoing investigations without clear resolution timelines, or incomplete disclosure of vulnerability impacts.
Medium: Conditions where communication is partially specific and responsive but lacks detailed technical analysis or timely acknowledgments.
Low: Conditions where communications are clear and responsive with adequate technical detail provided promptly.
Info: Conditions that provide basic information about vulnerabilities without significant impact on security posture.

Example Findings:

The company’s response to a reported vulnerability was vague, mentioning “security incident” without specific details or timeline for resolution.
A bug bounty program is acknowledged in communications but lacks detailed technical feedback or follow-up on researcher contributions.

Payout Structure Adequacy

Purpose: The Payout Structure Adequacy Scanner is designed to evaluate the alignment between security breach rewards and risks. Its primary purpose is to ensure that compensation adequately reflects the severity of incidents, helping in identifying potential misalignment where rewards might be disproportionately low compared to the risk or impact of breaches.

What It Detects:

Reward-Risk Misalignment: Identifies instances where reward amounts are significantly lower than the typical costs associated with security breaches and detects underfunded bounty programs relative to industry standards.
Severity-Compensation Imbalance: Analyzes whether rewards increase appropriately with the severity of reported vulnerabilities or incidents, checking for consistent scaling of rewards based on vulnerability classification (e.g., critical, high, medium).
Market Rate Disparities: Compares reward structures against market rates and industry benchmarks to ensure competitiveness and identifies patterns suggesting that rewards are below average compared to similar programs.
Lack of Transparency in Reward Criteria: Detects vague or unclear criteria for determining reward amounts and checks for the presence of detailed guidelines on how rewards are calculated based on vulnerability impact.
Historical Data Analysis: Reviews historical payout data to identify trends and patterns that may indicate misalignment over time, analyzing past payouts in relation to reported vulnerabilities to ensure consistency and fairness.

Inputs Required:

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

Business Impact: Ensuring that the compensation structure for security breaches is aligned with the risks and impacts of incidents is crucial for maintaining a robust cybersecurity posture. Misalignment can lead to underfunded programs, reduced incentive for reporting vulnerabilities, and potential legal or regulatory issues.

Risk Levels:

Critical: Conditions where there are significant discrepancies between reward amounts and the severity of reported breaches, potentially leading to severe financial implications or reputational damage.
High: Situations where rewards do not adequately reflect the risks associated with certain vulnerabilities, increasing the likelihood of unreported issues and potential exploitation.
Medium: Issues where the compensation structure is unclear or inadequately scaled based on vulnerability severity, which could lead to misinterpretation and potentially less effective risk management.
Low: Informal or transparent reward criteria that might not affect immediate risks but could indicate broader issues in program design and future compliance.
Info: Non-critical findings that do not directly impact security posture but may suggest areas for improvement in transparency, fairness, or compensation practices within the organization’s cybersecurity framework.

Example Findings:

A company with a critical vulnerability disclosed has a reward amount significantly lower than industry standards, indicating potential misalignment between risk and reward.
The payout structure lacks detailed criteria for determining rewards based on vulnerability severity, leading to inconsistent scaling of compensation across different breach scenarios.

Bounty Program Completeness

Purpose: The Bounty Program Completeness Scanner evaluates the comprehensiveness of a company’s bounty program by detecting missing vulnerability classes, technique restrictions, and limited attack vectors. This ensures that the program adequately covers potential security risks.

What It Detects:

Missing Vulnerability Classes: Identifies gaps in supported vulnerability types such as SQL injection, XSS, CSRF, server-side request forgery (SSRF), insecure deserialization, and command injection.
Technique Restrictions: Detects overly restrictive techniques that may limit the scope of valid submissions like no manual testing or only specific methods permitted.
Limited Attack Vectors: Evaluates if the program covers a broad range of attack vectors or focuses narrowly on specific areas such as web applications, APIs, mobile apps, etc.
Scope Clarity: Assesses whether the scope is clearly defined and comprehensive, identifying vague or ambiguous descriptions that could lead to misunderstandings about what is in or out of scope.
Reward Structure Fairness: Analyzes if the reward structure incentivizes a wide range of submissions or favors specific types of vulnerabilities disproportionately, ensuring transparent criteria for determining rewards based on severity and impact.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial as it ensures that a company’s bounty program not only exists but also effectively covers potential security risks and encourages ethical hacking practices across various attack vectors, thereby enhancing the overall security posture of the organization.

Risk Levels:

Critical: Conditions where there are significant gaps in vulnerability coverage or overly restrictive techniques that could deter legitimate submissions.
High: Conditions where specific types of vulnerabilities or methods are disproportionately rewarded without clear justification.
Medium: Conditions where certain attack vectors or scopes are not clearly defined, leading to potential misunderstandings among security researchers.
Low: Conditions where the bounty program is well-rounded and covers a broad spectrum of vulnerabilities with fair reward structures.
Info: Conditions where minor gaps exist that do not significantly impact the overall effectiveness of the bounty program but could be improved for better coverage.

Example Findings:

The bounty program does not explicitly mention CSRF as a supported vulnerability class, which may lead to submissions in this area being excluded from consideration.
There are strict restrictions on manual testing within the terms and conditions of the program, potentially limiting the ability of researchers to explore vulnerabilities through alternative methods.

Vulnerability Classification Bias

Purpose: The Vulnerability Classification Bias Scanner is designed to identify and highlight instances where organizations may downplay the severity of vulnerabilities, minimize their impact, or exaggerate the difficulty of exploitation. This documentation aims to assist in detecting potential biases that could affect security risk assessments and hinder effective vulnerability management strategies.

What It Detects:

Severity Downgrading: Detection of phrases indicating minimal risk (“minor issue”, “low-severity vulnerability”) and check for downplayed language around critical vulnerabilities.
Impact Minimization: Recognition of statements that minimize the impact of breaches or vulnerabilities, including vague descriptions of affected systems or data.
Exploitation Difficulty Exaggeration: Detection of claims of high exploitation difficulty without evidence and overemphasis on attacker sophistication.
Technical Justification Absence: Lack of detailed technical descriptions or evidence supporting vulnerability claims, along with vague statements about vulnerabilities.
Responsible Disclosure Practices: Recognition of responsible disclosure policies that may indicate a pattern of downplaying issues, including language suggesting quick resolution or minimal impact.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com) - This parameter is essential for scanning the specified website to detect vulnerability disclosure statements.
company_name (string): Company name for statement searching (e.g., “Acme Corporation”) - This helps in identifying relevant security-related statements on the company’s websites.

Business Impact: Detecting and addressing biases in vulnerability classification is crucial as it ensures that security risks are accurately assessed, leading to more effective remediation efforts and a stronger overall security posture.

Risk Levels:

Critical: Extremely severe conditions where vulnerabilities are either downplayed or the impact exaggerated without justification, potentially masking significant risks.
High: Severe conditions where vulnerabilities are minimized in severity or impact descriptions but may lack sufficient technical evidence to support such claims.
Medium: Moderate risk levels where there is a potential for underestimating vulnerability impacts or exaggerating exploitability with little supporting technical detail.
Low: Lower risk levels typically associated with clear, documented vulnerabilities that are appropriately assessed and addressed according to established security protocols.
Info: Informal findings indicating areas of concern but not severe enough to warrant immediate action, often requiring further investigation for confirmation or resolution.

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

Example Findings:

“Severity Downgrading Detected: This is a minor issue that does not require immediate attention. - Source: https://acme.com/security”
“Impact Minimization Detected: Limited data exposure occurred, no sensitive information was compromised. - Source: https://acme.com/data-breach”
“Exploitation Difficulty Exaggeration Detected: This requires advanced technical skills to exploit. - Source: https://acme.com/cyber-attack”
“Technical Justification Absence Detected: We identified a potential security issue that we are investigating. - Source: https://acme.com/newsroom”