Php – find php.ini

The correct way to avoid SQL injection attacks, no matter which database you use, is to separate the data from SQL, so that data stays data and will never be interpreted as commands by the SQL parser. It is possible to create SQL statement with correctly formatted data parts, but if you don't fully understand the details, you should always use prepared statements and parameterized queries. These are SQL statements that are sent to and parsed by the database server separately from any parameters. This way it is impossible for an attacker to inject malicious SQL.

You basically have two options to achieve this:

1. Using PDO (for any supported database driver):

    $stmt = $pdo->prepare('SELECT * FROM employees WHERE name = :name');
   
    $stmt->execute([ 'name' => $name ]);
   
    foreach ($stmt as $row) {
        // Do something with $row
    }
   

2. Using MySQLi (for MySQL):

    $stmt = $dbConnection->prepare('SELECT * FROM employees WHERE name = ?');
    $stmt->bind_param('s', $name); // 's' specifies the variable type => 'string'
   
    $stmt->execute();
   
    $result = $stmt->get_result();
    while ($row = $result->fetch_assoc()) {
        // Do something with $row
    }
   

If you're connecting to a database other than MySQL, there is a driver-specific second option that you can refer to (for example, pg_prepare() and pg_execute() for PostgreSQL). PDO is the universal option.

Correctly setting up the connection

Note that when using PDO to access a MySQL database real prepared statements are not used by default. To fix this you have to disable the emulation of prepared statements. An example of creating a connection using PDO is:

$dbConnection = new PDO('mysql:dbname=dbtest;host=127.0.0.1;charset=utf8', 'user', 'password');

$dbConnection->setAttribute(PDO::ATTR_EMULATE_PREPARES, false);
$dbConnection->setAttribute(PDO::ATTR_ERRMODE, PDO::ERRMODE_EXCEPTION);

In the above example the error mode isn't strictly necessary, but it is advised to add it. This way the script will not stop with a Fatal Error when something goes wrong. And it gives the developer the chance to catch any error(s) which are thrown as PDOExceptions.

What is mandatory, however, is the first setAttribute() line, which tells PDO to disable emulated prepared statements and use real prepared statements. This makes sure the statement and the values aren't parsed by PHP before sending it to the MySQL server (giving a possible attacker no chance to inject malicious SQL).

Although you can set the charset in the options of the constructor, it's important to note that 'older' versions of PHP (before 5.3.6) silently ignored the charset parameter in the DSN.

Explanation

The SQL statement you pass to prepare is parsed and compiled by the database server. By specifying parameters (either a ? or a named parameter like :name in the example above) you tell the database engine where you want to filter on. Then when you call execute, the prepared statement is combined with the parameter values you specify.

The important thing here is that the parameter values are combined with the compiled statement, not an SQL string. SQL injection works by tricking the script into including malicious strings when it creates SQL to send to the database. So by sending the actual SQL separately from the parameters, you limit the risk of ending up with something you didn't intend.

Any parameters you send when using a prepared statement will just be treated as strings (although the database engine may do some optimization so parameters may end up as numbers too, of course). In the example above, if the $name variable contains 'Sarah'; DELETE FROM employees the result would simply be a search for the string "'Sarah'; DELETE FROM employees", and you will not end up with an empty table.

Another benefit of using prepared statements is that if you execute the same statement many times in the same session it will only be parsed and compiled once, giving you some speed gains.

Oh, and since you asked about how to do it for an insert, here's an example (using PDO):

$preparedStatement = $db->prepare('INSERT INTO table (column) VALUES (:column)');

$preparedStatement->execute([ 'column' => $unsafeValue ]);

Can prepared statements be used for dynamic queries?

While you can still use prepared statements for the query parameters, the structure of the dynamic query itself cannot be parametrized and certain query features cannot be parametrized.

For these specific scenarios, the best thing to do is use a whitelist filter that restricts the possible values.

// Value whitelist
// $dir can only be 'DESC', otherwise it will be 'ASC'
if (empty($dir) || $dir !== 'DESC') {
   $dir = 'ASC';
}

If your goal is to use a profiler, use one of the suggested ones.

However, if you're in a hurry and you can manually interrupt your program under the debugger while it's being subjectively slow, there's a simple way to find performance problems.

Just halt it several times, and each time look at the call stack. If there is some code that is wasting some percentage of the time, 20% or 50% or whatever, that is the probability that you will catch it in the act on each sample. So, that is roughly the percentage of samples on which you will see it. There is no educated guesswork required. If you do have a guess as to what the problem is, this will prove or disprove it.

You may have multiple performance problems of different sizes. If you clean out any one of them, the remaining ones will take a larger percentage, and be easier to spot, on subsequent passes. This magnification effect, when compounded over multiple problems, can lead to truly massive speedup factors.

Caveat: Programmers tend to be skeptical of this technique unless they've used it themselves. They will say that profilers give you this information, but that is only true if they sample the entire call stack, and then let you examine a random set of samples. (The summaries are where the insight is lost.) Call graphs don't give you the same information, because

1. They don't summarize at the instruction level, and
2. They give confusing summaries in the presence of recursion.

They will also say it only works on toy programs, when actually it works on any program, and it seems to work better on bigger programs, because they tend to have more problems to find. They will say it sometimes finds things that aren't problems, but that is only true if you see something once. If you see a problem on more than one sample, it is real.

P.S. This can also be done on multi-thread programs if there is a way to collect call-stack samples of the thread pool at a point in time, as there is in Java.

P.P.S As a rough generality, the more layers of abstraction you have in your software, the more likely you are to find that that is the cause of performance problems (and the opportunity to get speedup).

Added: It might not be obvious, but the stack sampling technique works equally well in the presence of recursion. The reason is that the time that would be saved by removal of an instruction is approximated by the fraction of samples containing it, regardless of the number of times it may occur within a sample.

Another objection I often hear is: "It will stop someplace random, and it will miss the real problem". This comes from having a prior concept of what the real problem is. A key property of performance problems is that they defy expectations. Sampling tells you something is a problem, and your first reaction is disbelief. That is natural, but you can be sure if it finds a problem it is real, and vice-versa.

Added: Let me make a Bayesian explanation of how it works. Suppose there is some instruction I (call or otherwise) which is on the call stack some fraction f of the time (and thus costs that much). For simplicity, suppose we don't know what f is, but assume it is either 0.1, 0.2, 0.3, ... 0.9, 1.0, and the prior probability of each of these possibilities is 0.1, so all of these costs are equally likely a-priori.

Then suppose we take just 2 stack samples, and we see instruction I on both samples, designated observation o=2/2. This gives us new estimates of the frequency f of I, according to this:

Prior                                    
P(f=x) x  P(o=2/2|f=x) P(o=2/2&&f=x)  P(o=2/2&&f >= x)  P(f >= x | o=2/2)

0.1    1     1             0.1          0.1            0.25974026
0.1    0.9   0.81          0.081        0.181          0.47012987
0.1    0.8   0.64          0.064        0.245          0.636363636
0.1    0.7   0.49          0.049        0.294          0.763636364
0.1    0.6   0.36          0.036        0.33           0.857142857
0.1    0.5   0.25          0.025        0.355          0.922077922
0.1    0.4   0.16          0.016        0.371          0.963636364
0.1    0.3   0.09          0.009        0.38           0.987012987
0.1    0.2   0.04          0.004        0.384          0.997402597
0.1    0.1   0.01          0.001        0.385          1

                  P(o=2/2) 0.385                

The last column says that, for example, the probability that f >= 0.5 is 92%, up from the prior assumption of 60%.

Suppose the prior assumptions are different. Suppose we assume P(f=0.1) is .991 (nearly certain), and all the other possibilities are almost impossible (0.001). In other words, our prior certainty is that I is cheap. Then we get:

Prior                                    
P(f=x) x  P(o=2/2|f=x) P(o=2/2&& f=x)  P(o=2/2&&f >= x)  P(f >= x | o=2/2)

0.001  1    1              0.001        0.001          0.072727273
0.001  0.9  0.81           0.00081      0.00181        0.131636364
0.001  0.8  0.64           0.00064      0.00245        0.178181818
0.001  0.7  0.49           0.00049      0.00294        0.213818182
0.001  0.6  0.36           0.00036      0.0033         0.24
0.001  0.5  0.25           0.00025      0.00355        0.258181818
0.001  0.4  0.16           0.00016      0.00371        0.269818182
0.001  0.3  0.09           0.00009      0.0038         0.276363636
0.001  0.2  0.04           0.00004      0.00384        0.279272727
0.991  0.1  0.01           0.00991      0.01375        1

                  P(o=2/2) 0.01375                

Now it says P(f >= 0.5) is 26%, up from the prior assumption of 0.6%. So Bayes allows us to update our estimate of the probable cost of I. If the amount of data is small, it doesn't tell us accurately what the cost is, only that it is big enough to be worth fixing.

Yet another way to look at it is called the Rule Of Succession. If you flip a coin 2 times, and it comes up heads both times, what does that tell you about the probable weighting of the coin? The respected way to answer is to say that it's a Beta distribution, with average value (number of hits + 1) / (number of tries + 2) = (2+1)/(2+2) = 75%.

(The key is that we see I more than once. If we only see it once, that doesn't tell us much except that f > 0.)

So, even a very small number of samples can tell us a lot about the cost of instructions that it sees. (And it will see them with a frequency, on average, proportional to their cost. If n samples are taken, and f is the cost, then I will appear on nf+/-sqrt(nf(1-f)) samples. Example, n=10, f=0.3, that is 3+/-1.4 samples.)

Added: To give an intuitive feel for the difference between measuring and random stack sampling:
There are profilers now that sample the stack, even on wall-clock time, but what comes out is measurements (or hot path, or hot spot, from which a "bottleneck" can easily hide). What they don't show you (and they easily could) is the actual samples themselves. And if your goal is to find the bottleneck, the number of them you need to see is, on average, 2 divided by the fraction of time it takes. So if it takes 30% of time, 2/.3 = 6.7 samples, on average, will show it, and the chance that 20 samples will show it is 99.2%.

Here is an off-the-cuff illustration of the difference between examining measurements and examining stack samples. The bottleneck could be one big blob like this, or numerous small ones, it makes no difference.

Measurement is horizontal; it tells you what fraction of time specific routines take. Sampling is vertical. If there is any way to avoid what the whole program is doing at that moment, and if you see it on a second sample, you've found the bottleneck. That's what makes the difference - seeing the whole reason for the time being spent, not just how much.