Skip to content

Latest commit

 

History

History
41 lines (34 loc) · 7.19 KB

attack-surface.md

File metadata and controls

41 lines (34 loc) · 7.19 KB

Attack Surface Analysis for doctrine/lexer

Attack Surface: Denial of Service (DoS) via Complex Input

  • Description: Attackers can exploit the lexer's processing of complex or maliciously crafted input to cause excessive resource consumption (CPU, memory), leading to application unresponsiveness or failure. This is due to inefficient tokenization logic when handling specific input patterns.
  • How Lexer Contributes: The lexer's internal algorithms and tokenization rules, especially if they involve complex regular expressions or inefficient parsing logic, can be vulnerable to inputs designed to maximize processing time. Deeply nested structures, extremely long tokens, or patterns triggering backtracking in regex engines within the lexer are key contributors.
  • Example: A lexer parsing a language with nested expressions. An attacker provides input with thousands of levels of nesting, like [[[[...[expression]...]...]...]. The lexer spends excessive time traversing this deeply nested structure during tokenization, exhausting server resources and causing a DoS.
  • Impact: Application downtime, service unavailability, resource exhaustion, and potential financial loss due to service disruption.
  • Risk Severity: High to Critical
  • Mitigation Strategies:
    • Input Size Limits: Implement strict limits on the size and complexity of input data processed by the lexer (e.g., maximum string length, maximum nesting depth, maximum file size).
    • Lexer Operation Timeouts: Set timeouts for lexer operations to prevent indefinite processing. If tokenization exceeds the timeout, terminate the process and return an error.
    • Optimize Tokenization Rules (Especially Regex): Carefully review and optimize the lexer's tokenization rules, paying close attention to the efficiency of regular expressions. Avoid complex or backtracking-prone regex patterns. Consider simpler, more performant token recognition methods if possible.
    • Resource Limits: Implement system-level resource limits (CPU, memory) for the application to contain the impact of DoS attacks triggered by the lexer.

Attack Surface: Indirect Injection Vulnerabilities via Token Misinterpretation

  • Description: The lexer, by incorrectly interpreting input during tokenization, can indirectly create injection vulnerabilities in subsequent parsing or application logic. This occurs when the lexer fails to properly distinguish between code and data, or sanitize special characters, leading to misinterpretation of tokens by the parser.
  • How Lexer Contributes: If the lexer's tokenization process is flawed and doesn't correctly handle or sanitize special characters or code delimiters within user-controlled input, it can generate tokens that are then misinterpreted as commands or code by the parser. This misinterpretation is the lexer's direct contribution to the attack surface.
  • Example: A lexer for a templating language incorrectly tokenizes user-provided input. If the lexer fails to recognize and escape or sanitize characters like {{ or }} within user-provided text, it might tokenize malicious code embedded within the text as valid template commands. The template engine (parser), receiving these incorrect tokens, could then execute the malicious code, leading to Remote Code Execution (RCE).
  • Impact: Remote Code Execution (RCE), Cross-Site Scripting (XSS), or other injection-based attacks depending on how the misinterpreted tokens are used in the application's parsing and processing stages.
  • Risk Severity: High to Critical
  • Mitigation Strategies:
    • Correct Tokenization of Special Characters: Ensure the lexer is meticulously designed to correctly identify, tokenize, and potentially sanitize or escape special characters and code delimiters according to the language specification it is parsing.
    • Strict Input Validation Post-Tokenization (Context-Aware): Implement robust input validation after tokenization but before parsing or execution. This validation should be context-aware and verify that tokens are used as intended and are safe within the application's logic.
    • Principle of Least Privilege in Token Handling: Avoid directly executing or interpreting tokens as commands without explicit validation and authorization in the parser and application logic. Treat tokens derived from user input as potentially untrusted data unless proven otherwise.
    • Context-Aware Lexer Design: If feasible, design the lexer to be context-aware, allowing it to differentiate between code and data segments during tokenization and apply different processing rules accordingly.

Attack Surface: Regular Expression Denial of Service (ReDoS) in Token Definitions

  • Description: If doctrine/lexer's token definitions rely on regular expressions, poorly constructed regex patterns can be vulnerable to Regular Expression Denial of Service (ReDoS) attacks. Attackers can craft input that triggers excessive backtracking in these regexes, causing significant CPU consumption and DoS.
  • How Lexer Contributes: The lexer's configuration or internal implementation might use regular expressions to define token patterns. Vulnerable regexes within these definitions are the direct source of this attack surface. When the lexer attempts to match these regexes against malicious input, it can get stuck in exponential backtracking.
  • Example: A token definition in the lexer uses a regex like (a+)+c to match a specific token type. An attacker provides input like "aaaaaaaaaaaaaaaaaaaaaaaaaaaaab". When the lexer tries to match this input against the vulnerable regex, the regex engine enters a catastrophic backtracking scenario, consuming excessive CPU time and leading to a DoS.
  • Impact: Denial of Service (DoS), application unresponsiveness, resource exhaustion, potentially leading to service disruption.
  • Risk Severity: High
  • Mitigation Strategies:
    • Thorough ReDoS Analysis of Regexes: Conduct a detailed review of all regular expressions used in the lexer's token definitions specifically for ReDoS vulnerabilities. Utilize online ReDoS analyzers or static analysis tools to identify problematic regex patterns.
    • Regex Optimization and Simplification: Optimize regular expressions to prevent backtracking. Employ techniques like atomic groups (?>...) or possessive quantifiers *+, ++, ?+ to limit backtracking. Simplify complex regexes where possible.
    • Alternative Token Definition Methods: Explore alternatives to complex regular expressions for defining token patterns, especially for performance-critical parts of the lexer. Consider using more deterministic and efficient methods if suitable for the language being parsed.
    • Regex Engine Security Updates: Ensure the regex engine used by the lexer is up-to-date with the latest security patches, as regex engine vulnerabilities, including ReDoS issues, are sometimes addressed in updates.