content,
}
- local function spamassassin_callback(err, data, conn)
+ local function spamassassin_callback(err, data)
local function spamassassin_requery(error)
-- set current upstream to fail because an error occurred
--lua_util.debugm(rule.N, task, '%s: returned result: %s', rule.log_prefix, data)
--[[
- patterns tested against Spamassassin 3.4.2
-
- Spam: False ; 1.1 / 5.0
+ patterns tested against Spamassassin 3.4.6
X-Spam-Status: No, score=1.1 required=5.0 tests=HTML_MESSAGE,MIME_HTML_ONLY,
TVD_RCVD_SPACE_BRACKET,UNPARSEABLE_RELAY autolearn=no
- autolearn_force=no version=3.4.2
+ autolearn_force=no version=3.4.6
]] --
local header = string.gsub(tostring(data), "[\r\n]+[\t ]", " ")
--lua_util.debugm(rule.N, task, '%s: returned header: %s', rule.log_prefix, header)
- local symbols
- local spam_score
+ local symbols = ""
+ local spam_score = 0
for s in header:gmatch("[^\r\n]+") do
if string.find(s, 'X%-Spam%-Status: %S+, score') then
- local pattern_symbols = "X%-Spam%-Status: %S+, score%=(%d+%.%d+) .* tests=(.*,)(%s*%S+).*"
+ local pattern_symbols = "X%-Spam%-Status: %S+, score%=([%-%d%.]+)%s.*tests%=(.*,?)(%s*%S+)%sautolearn.*"
spam_score = string.gsub(s, pattern_symbols, "%1")
- lua_util.debugm(rule.N, task, '%s: spamd Spam line: %s', rule.log_prefix, spam_score)
symbols = string.gsub(s, pattern_symbols, "%2%3")
symbols = string.gsub(symbols, "%s", "")
end
end
+ lua_util.debugm(rule.N, task, '%s: spam_score: %s, symbols: %s, int spam_score: |%s|, type spam_score: |%s|',
+ rule.log_prefix, spam_score, symbols, tonumber(spam_score), type(spam_score))
+
if tonumber(spam_score) > 0 and #symbols > 0 and symbols ~= "none" then
if rule.extended == false then
common.yield_result(task, rule, symbols, spam_score)
common.save_cache(task, digest, rule, symbols, spam_score)
else
- local symbols_table = {}
- symbols_table = lua_util.str_split(symbols, ",")
+ local symbols_table = lua_util.str_split(symbols, ",")
lua_util.debugm(rule.N, task, '%s: returned symbols as table: %s', rule.log_prefix, symbols_table)
common.yield_result(task, rule, symbols_table, spam_score)