Submission Details
Severity:
The loss of precision affects the reward distribution ratio.
Summary
contracts/llm/LLMOracleManager.sol
In the avg function, precision loss occurs during the calculation process, causing the ans parameter to be smaller than the actual value.
Vulnerability Details
The following are the affected functions
function avg(uint256[] memory data) internal pure returns (uint256 ans) {
uint256 sum = 0;
for (uint256 i = 0; i < data.length; i++) {
sum += data[i];
}
ans = sum / data.length;
}
For example：
data = [1, 2, 2, 2, 2];
sum = 9;
ans = sum / 5 = 1;
The actual value of ans should be 1.8, closer to 2.
Impact
This affects the income allocated by the finalizeValidation function, making the income of validator and responder unreasonable.
function finalizeValidation(uint256 taskId) private {
TaskRequest storage task = requests[taskId];
// compute score for each generation
for (uint256 g_i = 0; g_i < task.parameters.numGenerations; g_i++) {
// get the scores for this generation, i.e. the g_i-th element of each validation
uint256[] memory scores = new uint256[]();
for (uint256 v_i = 0; v_i < task.parameters.numValidations; v_i++) {
scores[v_i] = validations[taskId][v_i].scores[g_i];
}
// compute the mean and standard deviation
(uint256 _stddev, uint256 _mean) = Statistics.stddev(scores);
// compute the score for this generation as the "inner-mean"
// and send rewards to validators that are within the range
uint256 innerSum = 0;
uint256 innerCount = 0;
for (uint256 v_i = 0; v_i < task.parameters.numValidations; ++v_i) {
uint256 score = scores[v_i];
if ((score >= _mean - _stddev) && (score <= _mean + _stddev)) {
innerSum += score;
innerCount++;
// send validation fee to the validator
_increaseAllowance(validations[taskId][v_i].validator, task.validatorFee);
}
}
// set score for this generation as the average of inner scores
uint256 inner_score = innerCount == 0 ? 0 : innerSum / innerCount;
responses[taskId][g_i].score = inner_score;
}
// now, we have the scores for each generation
// compute stddev for these and pick the ones above a threshold
uint256[] memory generationScores = new uint256[]();
for (uint256 g_i = 0; g_i < task.parameters.numGenerations; g_i++) {
generationScores[g_i] = responses[taskId][g_i].score;
}
// compute the mean and standard deviation
(uint256 stddev, uint256 mean) = Statistics.stddev(generationScores);
for (uint256 g_i = 0; g_i < task.parameters.numGenerations; g_i++) {
// ignore lower outliers
if (generationScores[g_i] >= mean - generationDeviationFactor * stddev) {
_increaseAllowance(responses[taskId][g_i].responder, task.generatorFee);
}
}
}
Tools Used
Recommendations
It is recommended to add the precision of 1e18 to the calculation to prevent precision loss.
The following is the recommended code modification:
function avg(uint256[] memory data) internal pure returns (uint256 ans) {
uint256 sum = 0;
for (uint256 i = 0; i < data.length; i++) {
sum += data[i];
}
ans = sum * 1e18 / data.length;
}
Prize pool breakdown
Total prize
21,000 USDC
nSLOC
1,03420 USDC / LOC
20,000 USDC
1,000 USDC
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