Class: CvssSuite::Cvss40CalcHelper

Inherits:
Object
  • Object
show all
Includes:
Cvss40Constants
Defined in:
lib/cvss_suite/cvss40/cvss40_calc_helper.rb

Overview

This class performs much of the score calculation logic for CVSS 4.0. It is heavily ported from the m and scoring methods in github.com/FIRSTdotorg/cvss-v4-calculator/blob/ac71416d935ad2ac87cd107ff87024561ea954a7/app.js#L121 This class has a few rubocop exclusions but maintaining parity with the ported

code seems more valuable than trying to follow the cops in this case.

Constant Summary

Constants included from Cvss40Constants

CvssSuite::Cvss40Constants::AC_LEVELS, CvssSuite::Cvss40Constants::AR_LEVELS, CvssSuite::Cvss40Constants::AT_LEVELS, CvssSuite::Cvss40Constants::AV_LEVELS, CvssSuite::Cvss40Constants::CR_LEVELS, CvssSuite::Cvss40Constants::E_LEVELS, CvssSuite::Cvss40Constants::IR_LEVELS, CvssSuite::Cvss40Constants::LOOKUP, CvssSuite::Cvss40Constants::MAX_COMPOSED, CvssSuite::Cvss40Constants::MAX_SEVERITY, CvssSuite::Cvss40Constants::PR_LEVELS, CvssSuite::Cvss40Constants::SA_LEVELS, CvssSuite::Cvss40Constants::SC_LEVELS, CvssSuite::Cvss40Constants::SI_LEVELS, CvssSuite::Cvss40Constants::UI_LEVELS, CvssSuite::Cvss40Constants::VA_LEVELS, CvssSuite::Cvss40Constants::VC_LEVELS, CvssSuite::Cvss40Constants::VI_LEVELS

Instance Method Summary collapse

Constructor Details

#initialize(cvss_property_bag) ⇒ Cvss40CalcHelper

Returns a new instance of Cvss40CalcHelper.



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 16

def initialize(cvss_property_bag)
  @cvss_property_bag = cvss_property_bag
end

Instance Method Details

#concat_and_stringify(first, second, third, fourth, fifth, sixth) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 355

def concat_and_stringify(first, second, third, fourth, fifth, sixth)
  String.new.concat(first.to_s, second.to_s, third.to_s, fourth.to_s, fifth.to_s, sixth.to_s)
end

#extract_value_metric(metric, str) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 375

def extract_value_metric(metric, str)
  # indexOf gives first index of the metric, we then need to go over its size
  index = str.index(metric) + metric.length + 1
  extracted = str.slice(index..)
  # remove what follow
  if extracted.index('/').positive?
    index_to_drop_after = extracted.index('/') - 1
    metric_val = truncate(extracted, index_to_drop_after)
  elsif extracted
    metric_val = extracted
    # case where it is the last metric so no ending /
  end

  metric_val
end

#get_eq_maxes(lookup, eq_value) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 347

def get_eq_maxes(lookup, eq_value)
  MAX_COMPOSED["eq#{eq_value}"][lookup[eq_value - 1]]
end

#m(metric) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 20

def m(metric)
  selected = @cvss_property_bag[metric]

  # If E=X it will default to the worst case i.e. E=A
  return 'A' if metric == 'E' && (selected == 'X' || selected.nil?)
  # If CR=X, IR=X or AR=X they will default to the worst case i.e. CR=H, IR=H and AR=H
  return 'H' if metric == 'CR' && (selected == 'X' || selected.nil?)
  # IR:X is the same as IR:H
  return 'H' if metric == 'IR' && (selected == 'X' || selected.nil?)
  # AR:X is the same as AR:H
  return 'H' if metric == 'AR' && (selected == 'X' || selected.nil?)

  # All other environmental metrics just overwrite base score values,
  # so if they’re not defined just use the base score value.
  if @cvss_property_bag.include?("M#{metric}")
    modified_selected = @cvss_property_bag["M#{metric}"]
    return modified_selected if modified_selected != 'X'
  end

  selected
end

#nil?(value) ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 351

def nil?(value)
  value.nil?
end

#parse_int(string_to_parse) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 371

def parse_int(string_to_parse)
  Integer(string_to_parse)
end

#retrieve_macro_vector ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 42

def retrieve_macro_vector
  # EQ1: 0-AV:N and PR:N and UI:N
  #      1-(AV:N or PR:N or UI:N) and not (AV:N and PR:N and UI:N) and not AV:P
  #      2-AV:P or not(AV:N or PR:N or UI:N)

  if m('AV') == 'N' && m('PR') == 'N' && m('UI') == 'N'
    eq1 = '0'
  elsif (m('AV') == 'N' || m('PR') == 'N' || m('UI') == 'N') &&
        !(m('AV') == 'N' && m('PR') == 'N' && m('UI') == 'N') &&
        (m('AV') != 'P')
    eq1 = '1'
  elsif m('AV') == 'P' ||
        !(m('AV') == 'N' ||
        m('PR') == 'N' ||
        m('UI') == 'N')
    eq1 = '2'
  end

  # EQ2: 0-(AC:L and AT:N)
  #      1-(not(AC:L and AT:N))

  if m('AC') == 'L' && m('AT') == 'N'
    eq2 = '0'
  elsif !(m('AC') == 'L' && m('AT') == 'N')
    eq2 = '1'
  end

  # EQ3: 0-(VC:H and VI:H)
  #      1-(not(VC:H and VI:H) and (VC:H or VI:H or VA:H))
  #      2-not (VC:H or VI:H or VA:H)
  if m('VC') == 'H' && m('VI') == 'H'
    eq3 = '0'
  elsif !(m('VC') == 'H' && m('VI') == 'H') &&
        (m('VC') == 'H' || m('VI') == 'H' || m('VA') == 'H')
    eq3 = '1'
  elsif !(m('VC') == 'H' || m('VI') == 'H' || m('VA') == 'H')
    eq3 = '2'
  end

  # EQ4: 0-(MSI:S or MSA:S)
  #      1-not (MSI:S or MSA:S) and (SC:H or SI:H or SA:H)
  #      2-not (MSI:S or MSA:S) and not (SC:H or SI:H or SA:H)

  if m('MSI') == 'S' || m('MSA') == 'S'
    eq4 = '0'
  elsif !(m('MSI') == 'S' || m('MSA') == 'S') &&
        (m('SC') == 'H' || m('SI') == 'H' || m('SA') == 'H')
    eq4 = '1'
  elsif !(m('MSI') == 'S' || m('MSA') == 'S') &&
        !(m('SC') == 'H' || m('SI') == 'H' || m('SA') == 'H')
    eq4 = '2'
  end

  # EQ5: 0-E:A
  #      1-E:P
  #      2-E:U

  eq5 = case m('E')
        when 'A'
          '0'
        when 'P'
          '1'
        when 'U'
          '2'
        else
          # brphelps TODO added figure it out
          '0'
        end

  # EQ6: 0-(CR:H and VC:H) or (IR:H and VI:H) or (AR:H and VA:H)
  #      1-not[(CR:H and VC:H) or (IR:H and VI:H) or (AR:H and VA:H)]

  if (m('CR') == 'H' && m('VC') == 'H') ||
     (m('IR') == 'H' && m('VI') == 'H') ||
     (m('AR') == 'H' && m('VA') == 'H')
    eq6 = '0'
  elsif !((m('CR') == 'H' && m('VC') == 'H') ||
      (m('IR') == 'H' && m('VI') == 'H') ||
      (m('AR') == 'H' && m('VA') == 'H'))
    eq6 = '1'
  end

  eq1 + eq2 + eq3 + eq4 + eq5 + eq6
end

#score ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 127

def score
  # The following defines the index of each metric's values.
  # It is used when looking for the highest vector part of the
  # combinations produced by the MacroVector respective highest vectors.

  macro_vector = retrieve_macro_vector

  # Exception for no impact on system (shortcut)
  return 0.0 if %w[VC VI VA SC SI SA].all? { |metric| m(metric) == 'N' }

  value = LOOKUP[macro_vector].to_d

  # 1. For each of the EQs:
  #   a. The maximal scoring difference is determined as the difference
  #      between the current MacroVector and the lower MacroVector.
  #     i. If there is no lower MacroVector the available distance is
  #        set to nil and then ignored in the further calculations.
  eq1_val = parse_int(macro_vector[0])
  eq2_val = parse_int(macro_vector[1])
  eq3_val = parse_int(macro_vector[2])
  eq4_val = parse_int(macro_vector[3])
  eq5_val = parse_int(macro_vector[4])
  eq6_val = parse_int(macro_vector[5])

  # compute next lower macro, it can also not exist
  eq1_next_lower_macro = concat_and_stringify(eq1_val + 1, eq2_val, eq3_val, eq4_val, eq5_val, eq6_val)
  eq2_next_lower_macro = concat_and_stringify(eq1_val, eq2_val + 1, eq3_val, eq4_val, eq5_val, eq6_val)

  # eq3 and eq6 are related
  if eq3_val == 1 && eq6_val == 1
    # 11 --> 21
    eq3eq6_next_lower_macro = concat_and_stringify(eq1_val, eq2_val, eq3_val + 1, eq4_val, eq5_val, eq6_val)
  elsif eq3_val.zero? && eq6_val == 1
    # 01 --> 11
    eq3eq6_next_lower_macro = concat_and_stringify(eq1_val, eq2_val, eq3_val + 1, eq4_val, eq5_val, eq6_val)
  elsif eq3_val == 1 && eq6_val.zero?
    # 10 --> 11
    eq3eq6_next_lower_macro = concat_and_stringify(eq1_val, eq2_val, eq3_val, eq4_val, eq5_val, eq6_val + 1)
  elsif eq3_val.zero? && eq6_val.zero?
    # 00 --> 01
    # 00 --> 10
    eq3eq6_next_lower_macro_left = concat_and_stringify(eq1_val, eq2_val, eq3_val, eq4_val, eq5_val, eq6_val + 1)
    eq3eq6_next_lower_macro_right = concat_and_stringify(eq1_val, eq2_val, eq3_val + 1, eq4_val, eq5_val, eq6_val)
  else
    # 21 --> 32 (do not exist)
    eq3eq6_next_lower_macro = concat_and_stringify(eq1_val, eq2_val, eq3_val + 1, eq4_val, eq5_val, eq6_val + 1)
  end

  eq4_next_lower_macro = concat_and_stringify(eq1_val, eq2_val, eq3_val, eq4_val + 1, eq5_val, eq6_val)
  eq5_next_lower_macro = concat_and_stringify(eq1_val, eq2_val, eq3_val, eq4_val, eq5_val + 1, eq6_val)

  # get their score, if the next lower macro score do not exist the result is NaN
  score_eq1_next_lower_macro = LOOKUP[eq1_next_lower_macro]
  score_eq2_next_lower_macro = LOOKUP[eq2_next_lower_macro]

  if eq3_val.zero? && eq6_val.zero?
    # multiple path take the one with higher score
    score_eq3eq6_next_lower_macro_left = LOOKUP[eq3eq6_next_lower_macro_left]
    score_eq3eq6_next_lower_macro_right = LOOKUP[eq3eq6_next_lower_macro_right]

    score_eq3eq6_next_lower_macro = if score_eq3eq6_next_lower_macro_left > score_eq3eq6_next_lower_macro_right
                                      score_eq3eq6_next_lower_macro_left
                                    else
                                      score_eq3eq6_next_lower_macro_right
                                    end
  else
    score_eq3eq6_next_lower_macro = LOOKUP[eq3eq6_next_lower_macro]
  end

  score_eq4_next_lower_macro = LOOKUP[eq4_next_lower_macro]
  score_eq5_next_lower_macro = LOOKUP[eq5_next_lower_macro]

  #   b. The severity distance of the to-be scored vector from a
  #      highest severity vector in the same MacroVector is determined.
  eq1_maxes = get_eq_maxes(macro_vector, 1)
  eq2_maxes = get_eq_maxes(macro_vector, 2)
  eq3_eq6_maxes = get_eq_maxes(macro_vector, 3)[macro_vector[5]]
  eq4_maxes = get_eq_maxes(macro_vector, 4)
  eq5_maxes = get_eq_maxes(macro_vector, 5)

  # compose them
  max_vectors = []
  eq1_maxes.each do |eq1_max|
    eq2_maxes.each do |eq2_max|
      eq3_eq6_maxes.each do |eq3_eq6_max|
        eq4_maxes.each do |eq4_max|
          eq5_maxes.each do |eq5max|
            max_vectors.push(eq1_max + eq2_max + eq3_eq6_max + eq4_max + eq5max)
          end
        end
      end
    end
  end

  severity_distance_av = severity_distance_pr = severity_distance_ui = 0
  severity_distance_ac = severity_distance_at = severity_distance_vc = 0
  severity_distance_vi = severity_distance_va = severity_distance_sc = 0
  severity_distance_si = severity_distance_sa = severity_distance_cr = 0
  severity_distance_ir = severity_distance_ar = 0

  # Find the max vector to use i.e. one in the combination of all the highests
  # that is greater or equal (severity distance) than the to-be scored vector.
  max_vectors.each do |max_vector|
    severity_distance_av = AV_LEVELS[m('AV')] - AV_LEVELS[extract_value_metric('AV', max_vector)]
    severity_distance_pr = PR_LEVELS[m('PR')] - PR_LEVELS[extract_value_metric('PR', max_vector)]
    severity_distance_ui = UI_LEVELS[m('UI')] - UI_LEVELS[extract_value_metric('UI', max_vector)]

    severity_distance_ac = AC_LEVELS[m('AC')] - AC_LEVELS[extract_value_metric('AC', max_vector)]
    severity_distance_at = AT_LEVELS[m('AT')] - AT_LEVELS[extract_value_metric('AT', max_vector)]

    severity_distance_vc = VC_LEVELS[m('VC')] - VC_LEVELS[extract_value_metric('VC', max_vector)]
    severity_distance_vi = VI_LEVELS[m('VI')] - VI_LEVELS[extract_value_metric('VI', max_vector)]
    severity_distance_va = VA_LEVELS[m('VA')] - VA_LEVELS[extract_value_metric('VA', max_vector)]

    severity_distance_sc = SC_LEVELS[m('SC')] - SC_LEVELS[extract_value_metric('SC', max_vector)]
    severity_distance_si = SI_LEVELS[m('SI')] - SI_LEVELS[extract_value_metric('SI', max_vector)]
    severity_distance_sa = SA_LEVELS[m('SA')] - SA_LEVELS[extract_value_metric('SA', max_vector)]

    severity_distance_cr = subtract_or_nil(CR_LEVELS[m('CR')], CR_LEVELS[extract_value_metric('CR', max_vector)])
    severity_distance_ir = subtract_or_nil(IR_LEVELS[m('IR')], IR_LEVELS[extract_value_metric('IR', max_vector)])
    severity_distance_ar = subtract_or_nil(AR_LEVELS[m('AR')], AR_LEVELS[extract_value_metric('AR', max_vector)])

    # if any is less than zero this is not the right max
    if [severity_distance_av, severity_distance_pr, severity_distance_ui, severity_distance_ac,
        severity_distance_at, severity_distance_vc, severity_distance_vi, severity_distance_va,
        severity_distance_sc, severity_distance_si, severity_distance_sa,
        severity_distance_cr,
        severity_distance_ir, severity_distance_ar].compact.any?(&:negative?)
      next
    end

    # if multiple maxes exist to reach it it is enough the first one
    break
  end

  current_severity_distance_eq1 = (severity_distance_av.to_d + severity_distance_pr + severity_distance_ui).to_f
  current_severity_distance_eq2 = severity_distance_ac + severity_distance_at
  current_severity_distance_eq3eq6 = sum_or_nil([severity_distance_vc, severity_distance_vi, severity_distance_va,
                                                 severity_distance_cr, severity_distance_ir, severity_distance_ar])
  current_severity_distance_eq4 = severity_distance_sc + severity_distance_si + severity_distance_sa

  step = 0.1

  # if the next lower macro score do not exist the result is Nan
  # Rename to maximal scoring difference (aka MSD)
  available_distance_eq1 = score_eq1_next_lower_macro ? value - score_eq1_next_lower_macro : nil
  available_distance_eq2 = score_eq2_next_lower_macro ? value - score_eq2_next_lower_macro : nil
  available_distance_eq3eq6 = score_eq3eq6_next_lower_macro ? value - score_eq3eq6_next_lower_macro : nil
  available_distance_eq4 = score_eq4_next_lower_macro ? value - score_eq4_next_lower_macro : nil
  available_distance_eq5 = score_eq5_next_lower_macro ? value - score_eq5_next_lower_macro : nil

  # some of them do not exist, we will find them by retrieving the score. If score null then do not exist
  n_existing_lower = 0

  normalized_severity_eq1 = 0
  normalized_severity_eq2 = 0
  normalized_severity_eq3eq6 = 0
  normalized_severity_eq4 = 0
  normalized_severity_eq5 = 0

  # multiply by step because distance is pure
  max_severity_eq1 = MAX_SEVERITY['eq1'][eq1_val] * step
  max_severity_eq2 = MAX_SEVERITY['eq2'][eq2_val] * step
  max_severity_eq3eq6 = MAX_SEVERITY['eq3eq6'][eq3_val][eq6_val] * step
  max_severity_eq4 = MAX_SEVERITY['eq4'][eq4_val] * step

  #   c. The proportion of the distance is determined by dividing
  #      the severity distance of the to-be-scored vector by the depth
  #      of the MacroVector.
  #   d. The maximal scoring difference is multiplied by the proportion of
  #      distance.
  unless nil?(available_distance_eq1)
    n_existing_lower += 1
    percent_to_next_eq1_severity = current_severity_distance_eq1 / max_severity_eq1
    normalized_severity_eq1 = available_distance_eq1 * percent_to_next_eq1_severity
  end

  unless nil?(available_distance_eq2)
    n_existing_lower += 1
    percent_to_next_eq2_severity = current_severity_distance_eq2 / max_severity_eq2
    normalized_severity_eq2 = available_distance_eq2 * percent_to_next_eq2_severity
  end

  unless nil?(available_distance_eq3eq6)
    n_existing_lower += 1
    percent_to_next_eq3eq6_severity = current_severity_distance_eq3eq6 / max_severity_eq3eq6
    normalized_severity_eq3eq6 = available_distance_eq3eq6 * percent_to_next_eq3eq6_severity
  end

  unless nil?(available_distance_eq4)
    n_existing_lower += 1
    percent_to_next_eq4_severity = current_severity_distance_eq4 / max_severity_eq4
    normalized_severity_eq4 = available_distance_eq4 * percent_to_next_eq4_severity
  end

  unless nil?(available_distance_eq5)
    # for eq5 is always 0 the percentage
    n_existing_lower += 1
    percent_to_next_eq5_severity = 0
    normalized_severity_eq5 = available_distance_eq5 * percent_to_next_eq5_severity
  end

  # 2. The mean of the above computed proportional distances is computed.
  mean_distance = if n_existing_lower.zero?
                    0
                  else # sometimes we need to go up but there is nothing there, or down
                    # but there is nothing there so it's a change of 0.
                    (normalized_severity_eq1 + normalized_severity_eq2 + normalized_severity_eq3eq6 +
                                    normalized_severity_eq4 + normalized_severity_eq5) / n_existing_lower
                  end

  # 3. The score of the vector is the score of the MacroVector
  #    (i.e. the score of the highest severity vector) minus the mean
  #    distance so computed. This score is rounded to one decimal place.
  value -= mean_distance
  value = 0.0 if value.negative?
  value = 10.0 if value > 10
  value.round(1).to_f
end

#subtract_or_nil(left, right) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 365

def subtract_or_nil(left, right)
  return nil if left.nil? || right.nil?

  left - right
end

#sum_or_nil(values) ⇒ Object 



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 359

def sum_or_nil(values)
  return nil if values.any?(&:nil?)

  values.sum
end

#truncate(string_to_truncate, truncate_to) ⇒ Object

rails defines this method on String, so we need to avoid polluting the

String class to preserve Rails behavior.



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# File 'lib/cvss_suite/cvss40/cvss40_calc_helper.rb', line 393

def truncate(string_to_truncate, truncate_to)
  return string_to_truncate.dup unless string_to_truncate.length > truncate_to

  (string_to_truncate[0, truncate_to + 1]).to_s
end