Comment attraper des pirates dans votre code

Que feriez-vous si des pirates abusaient de votre logiciel en production?

Ce n'est pas une question hypothétique. Ils le font probablement en ce moment.

Vous pensez peut-être à tous les choix de conception sécurisés que vous avez faits ou aux techniques préventives que vous avez appliquées, donc il n'y a pas de quoi s'inquiéter.

Si tel est le cas, c'est super - même s'il y a toujours des choses qui sont négligées, vous devriez toujours penser à la sécurité de votre système.

Mais il y a une énorme différence entre la prévention des bogues de sécurité et le pardon des tentatives malveillantes.

Que diriez-vous d'attraper et d'agir sur les pirates qui tentent de s'introduire dans notre logiciel? Dans cet article, je vais essayer de vous donner des exemples pratiques et simples de capture précoce des comportements typiques des hackers dans votre code.

Pourquoi attraper des tentatives malveillantes?

La prévention des bogues de sécurité ne suffit-elle pas? Je peux vous entendre dire: «Tant que j'écris du code sécurisé, je me fiche de savoir si les pirates jouent avec mon logiciel solide comme le roc ou non. Alors, pourquoi devrais-je me soucier des tentatives malveillantes? »

Répondons d'abord à cette question valable.

Un logiciel quelque peu complexe est difficile à sécuriser en permanence. Plus de complexité signifie plus de faiblesses potentielles pour un pirate informatique lors de la conception, de la mise en œuvre, du déploiement ou de la maintenance du code.

Regardez simplement les chiffres CVE au fil des ans. C'est beaucoup:

De plus, en raison de sa nature, un bogue de sécurité n'est pas seulement un élément régulier de votre backlog. Il y a des conséquences désagréables si une vulnérabilité est exploitée: une perte de confiance, une mauvaise réputation ou même une perte financière.

Ainsi, les meilleures pratiques de sécurité telles que la norme de vérification de la sécurité des applications OWASP (ASVS) ou les directives de codage sécurisé de Mozilla existent afin d'aider les développeurs à produire des logiciels sécurisés.

Cependant, étant donné que de nouvelles façons de contourner les contrôles de sécurité existants ou que de nouvelles faiblesses apparaissent presque quotidiennement, il existe un consensus autour de la communauté de la sécurité sur le fait qu'il n'y a pas de sécurité à 100%. Nous devons donc toujours être vigilants et réactifs aux nouvelles et améliorations en matière de sécurité.

Il y a aussi une autre chose que nous pouvons faire pour garantir la sécurité des logiciels: remarquer les pirates le plus tôt possible, avant qu'ils ne fassent quelque chose que nous ne nous attendons pas ou ne savons même pas. De plus, suivre leur comportement malveillant sur une longue période nous rend plus proactifs.

Il existe une notion populaire de Security Operations Center (SOC) dans ce sens - les SOC sont un type d'équipe dans une organisation qui est externalisée ou en interne. Leur travail consiste à surveiller en permanence l'état de sécurité de l'organisation. Ils le font en détectant, analysant et en répondant aux incidents de cybersécurité.

Les équipes SOC recherchent les activités anormales, y compris les anomalies de sécurité logicielle. L'idée de remarquer et de répondre à une cyber-attaque réussie ou échouée donne aux entreprises un avantage contre les menaces, ce qui réduit en fin de compte le temps de réponse aux attaques grâce à une surveillance continue.

Un SOC n'est fort que grâce à l'apport riche et de qualité qu'il reçoit de différentes sources de composants informatiques. Notre logiciel étant également une partie importante de l'inventaire, les alarmes de sécurité appropriées dues aux comportements anormaux envoyées par nos logiciels aux équipes SOC sont inestimables.

Comment vérifier les comportements anormaux

Voici un certain nombre de vérifications et de contrôles que nous pouvons implémenter dans tout notre code qui révèlent des comportements malveillants et anormaux.

Avant de commencer, j'aimerais souligner que je ne présente pas ici de solutions compliquées comme le pare-feu d'application Web (WAF). Au lieu de cela, je vais simplement essayer de vous montrer que de simples conditions, une gestion intelligente des exceptions et des actions similaires avec peu ou pas d'effort dans votre code peuvent vous aider à remarquer les comportements anormaux dès qu'ils se produisent.

Allons creuser.

Longueur nulle ou retours nuls

La première action que nous pouvons entreprendre pour détecter une action malveillante consiste à vérifier les agrégats de longueur nulle ou les retours nuls.

Voici un bloc de code simple pour illustrer ce point:

Receipt receipt = GetReceipt(transferId); if (receipt == null) { // what does this mean? // log, notify, alarm }

Ici, nous essayons d'accéder à la réception d'un certain transfert fourni par nos utilisateurs finaux via le transferIdparamètre.

Afin d'empêcher quiconque d'accéder aux reçus de quelqu'un d'autre, supposons qu'à l'intérieur de la GetReceiptméthode, notre développeur est suffisamment intelligent pour vérifier si le transferIdfichier appartient vraiment à l'utilisateur actuel.

Vérifier la propriété est une bonne pratique de sécurité.

Supposons en outre que nous sommes sûrs par conception que chaque transfert doit avoir au moins un reçu associé, donc n'en obtenir aucun à l'exécution est suspect. Pourquoi? Parce qu'obtenir un reçu vide signifie que le fourni transferIdn'appartient à aucun transfert exécuté par l'utilisateur actuel.

En d'autres termes, l'utilisateur actuel a fourni un transferIdcode falsifié à notre code et attend de voir le contenu si cela transferIdse rapporte à la transaction de quelqu'un d'autre.

Et puisque nous avons le contrôle de propriété approprié, la GetReceiptméthode renvoie un reçu vide ou nul. C'est là que nous devons prendre des mesures de sécurité.

Je n'entrerai pas dans les détails des actions de sécurité dans cet article. Cependant, la journalisation de la sécurité et / ou l'envoi de notifications détaillées, les systèmes d'information de sécurité et de gestion des événements (SIEM) en sont deux.

Voici un autre exemple de la façon dont la vérification de la valeur nulle nous permet de saisir une tentative malveillante.

Considérez que nous avons les trois critères d' évaluation suivants, ShowReceipt, Successet Error:

// ShowReceipt endpoint if(CurrentUser.Owns(receiptId)) { Session["receiptid"] = receiptId; redirect "Success"; } else { redirect "Error"; }
// Success endpoint receiptId = Session["receiptid"]; return ReadReceipt(receiptId);
// Error endpoint return "Error";

Il s'agit d'une application simple qui montre le contenu du reçu d'un utilisateur.

En ShowReceipt, la première ligne est importante. Il vérifie si l'utilisateur final nous envoie un valide receiptIdpour voir son contenu. Sans ce contrôle, un utilisateur malveillant peut en fournir receiptIdet accéder au contenu.

La place de l'énoncé dans la troisième ligne est cependant tout aussi importante. Si nous déplaçons cette ligne juste avant l'instruction if, cela ne casserait rien. Cependant, cela créerait le même problème de sécurité que nous essayions d'éviter en vérifiant si l'utilisateur final demande un reçu valide ou non.

Veuillez prendre un moment pour vous assurer que vous comprenez pourquoi c'est le cas.

Maintenant, c'est une bonne idée que nous avons placé cette ligne au bon endroit et cela crée une autre occasion de remarquer les tentatives malveillantes. Ensuite, dans le Successpoint de terminaison, qu'est-ce que cela signifie si nous obtenons null à receiptIdpartir du Session?

Cela signifie que quelqu'un appelle ce point de terminaison, juste après avoir fait une demande de ShowReceiptpoint de terminaison avec celui de quelqu'un d'autre receiptId. Même s'ils ont été Errorredirigés à cause du contrôle de propriété!

Bien sûr, avec le contrôle que nous avons en première ligne, c'est impossible.

Ainsi, le Successpoint de terminaison est un endroit agréable pour écrire une entrée de journal de sécurité et envoyer des notifications à nos solutions de surveillance lorsque nous obtenons une valeur nulle receiptIddu Session.

// Success endpoint (Revisited) receiptId = Session["receiptid"]; if(receiptId == null) { // log, notify, alarm } return ReadReceipt(receiptId);

Gestion des exceptions ciblée

Exception handling is maybe the most important mechanism for developers to respond to any anomalous condition during the execution of the program.

Most of the time the main opportunity it provides is cleaning up resources that were borrowed such as file/network streams or database connections upon unexpected problems. This is a fail-safe behavior that lets us write more reliable programs.

In parallel we can effectively use runtime exceptions to notice malicious attempts towards our software.

Here are some popular sources of weakness where we can utilize related exceptions to notice fishy behavior:

  • Deserialization
  • Cryptography
  • XML Parsing
  • Regular Expression
  • Arithmetic Operations

The list is not complete, of course. And here I’ll go through only a few of these APIs.

Let’s start with Regular Expressions. Here’s a code block that applies a strict validation method on a user input:

if(!Regex.IsMatch(query.Search, @"^([a-zA-Z0-9]+ ?)+$")) { return RedirectToAction("Error"); }

The regular expression pattern used here is a solid whitelist one, which means it checks what is expected as an input. Not the other insecure way around, which is checking what is known to be bad.

Still, here’s a much secure version of the same code block:

if(!Regex.IsMatch(query.Search, @"^([a-zA-Z0-9]+ ?)+$", RegexOptions.Compiled, TimeSpan.FromSeconds(10))) { return RedirectToAction("Error"); }

This is an overloaded version of the IsMatch method of which the last argument is the key.

It enforces that the execution of the regular expression during runtime can not exceed 10 seconds. If it does, that means something suspicious is going on since the pattern used is not that complicated.

There’s an actual security weakness that might be used to abuse this pattern called ReDoS, though I won't go into the details of it here. But in short, an end-user can send the following string as the search parameter and make our back-end miserable, spending an awful amount of CPU power in vain.

Notice the quotation mark at the end (and don’t try this in production!):

AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA!

The question is, what happens when the execution time actually exceeds 10 seconds?

The .NET environment throws an exception, namely RegexMatchTimeoutException. So, if we specifically catch this exception, we now have the opportunity to report this suspicious incident or do something about it.

Here’s the final code block to that end:

try { if(!Regex.IsMatch(query.Search, @"^([a-zA-Z0-9]+ ?)+$", RegexOptions.Compiled, TimeSpan.FromSeconds(10))) { return RedirectToAction("Error"); } } catch(RegexMatchTimeoutException rmte) { // log, notify, alarm }

Another important venue where we can utilize exceptions is XML parsing. Here’s an example code block:

XmlReader xmlReader = XmlReader.Create(input); var root = XDocument.Load(xmlReader, LoadOptions.PreserveWhitespace);

The input XML is fed into XmlReader.Create, and then we get the root element. Hackers can abuse this piece of code by providing some malicious XML files, which, when parsed by the above code, gives ownership of our servers to them.

Scary, right? The security bug is called XML External Entity (XXE) attack, and as with the Regular Expression exploit, I won't go into all the details here.

However, in order to prevent that super critical weakness, we ignore the usage of Document Type Definitions (DTD) through the XmlReaderSettings. So now, there’s no possibility of XXE security bugs anymore.

Here’s the secure version:

XmlReaderSettings settings = new XmlReaderSettings(); settings.DtdProcessing = DtdProcessing.Ignore; XmlReader xmlReader = XmlReader.Create(input, settings); var root = XDocument.Load(xmlReader, LoadOptions.PreserveWhitespace);

We can leave the code just like this and move on. However, if a hacker still tries to abuse this attack in vain, it's better that we can catch this behavior and produce an invaluable security alert:

try { XmlReaderSettings settings = new XmlReaderSettings(); settings.DtdProcessing = DtdProcessing.Ignore; XmlReader xmlReader = XmlReader.Create(input, settings); var root = XDocument.Load(xmlReader, LoadOptions.PreserveWhitespace); } catch(XmlException xe) { // log, notify, alarm }

Moreover, in order to prevent false positives, you can further customize the catch block by using the message content provided by the XmlException instance.

There’s a general programming best practice that denies using generic Exception types in catch blocks. What we have shown is also a good supporting case for this. Same goes with another best practice that denies using empty catch blocks, which is effectively doing nothing when an abnormal behavior occurs in our code.

Apparently though, instead of empty catch blocks, here we have a very solid opportunity to react to malicious attempts.

Normalization on Inputs

By definition, normalization is to get the simplest form of something. In fact, canonicalization is the term used for this purpose. But it is hard to pronounce, so, let's stick to normalization.

Of course, “the simplest form of something” is a little bit abstract. What do we mean by the “simplest form”?

It is always good to show by example. Here is a string:

%3cscript%3e

According to the URL encoding, this string is not in its simplest form. Because if we apply URL decoding on it, we get this one:

This is the simplest form of the original string according to URL encoding transformation standard.

How do we know that? We know it not because it is understandable to us now. We know it because if we apply URL decoding again, we will get the same string:

And that means URL decoding does not successfully transform it anymore. We hit the simplest form. Normalization can take more than one step, as originally the encoding might be applied more than once.

URL encoding is just one example of the transformation used for normalization, or in other words, decoding. HTML encoding, JavaScript encoding, and CSS encoding are other important encoding/decoding methods widely used for normalization.

Over the years, attackers find genuine techniques to bypass defense systems. And one of the most prevalent techniques they utilize is encoding. They use crazy encoding techniques on their original malicious inputs, in order to fool defenses around applications.

History is full of these demonstrations, and you can read the details of one of the most famous ones called Microsoft’s infamous IIS dotdot attack that took place in the early 2000s.

Since hackers rely on encoding techniques substantially when they are sending malicious inputs, normalization can be one of the most effective and easy ways to seize them.

Here is the rule of thumb: we recursively apply URL/HTML/CSS/JavaScript decoding to user input until the output no longer changes. And if the output is a different string than the original input, that means we may have a possible malicious request.

Here’s a simplified version of legendary OWASP ESAPI Java that implements this idea:

int foundCount = 0; boolean clean = false; while(!clean) { clean = true; // whatever codes you want; URL/Javascript/HTML/... Iterator i = codecs.iterator(); while (i.hasNext()) { Codec codec = (Codec)i.next(); String old = input; input = codec.decode(input); if (!old.equals(input)) { if (clean) { foundCount++; } clean = false; } } }

When the code block ends, if the value of foundCount is bigger or equal to 2, that means what? It means someone is sending multiple encoded input to our application, and the odds of this happening is really rare.

Normal users do not send multiple encoded strings to our application. There is a high probability that this is a malicious user. We have to log this event with the original input for further analysis.

The above mechanism, while part of the software itself, functions like a filter in front of the application. It runs on every untrusted input and gives us an opportunity to know about malicious attempts.

However, you may be suspicious about the additional delay this way of validation incurs. I understand if you don’t want to opt-in.

Here's another example of using normalization as a means to seize malicious attempts during file uploads or downloads. Consider the following code:

if (!String.IsNullOrEmpty(fileName)) { fileName = new FileInfo(fileName).Name; string path = @"E:\uploaded_files\" + fileName; if (File.Exists(path)) { response.ContentType = "image/jpg"; response.BinaryWrite(File.ReadAllBytes(path)); } }

Here we get a fileName parameter from our client, locate the image it points to, read, and present the content. This is a download example. It might also have been an upload scenario.

Nevertheless, in order to prevent the client manipulating the fileName parameter to their heart’s content, we utilize the Name property of the FileInfo class. This will only get the name part of the fileName, even if the client sends us anything other than what we expect (i.e. a file name with forged paths such as below):

../../WebSites/Cross/Web.config

Here the malicious client wants to read the contents of a sensitive Web.Config file by using our code. Getting only the file name part, we get rid of this possibility.

That is good but there is still something we can do:

if (!String.IsNullOrEmpty(fileName)) { string normalizedFileName = new FileInfo(fileName).Name; if (normalizedFileName != fileName) { // log, notify, alarm response = ResponseStatus.Unauthorized; } string path = @"E:\uploaded_files\" + fileName; if (File.Exists(path)) { response.ContentType = "image/jpg"; response.BinaryWrite(File.ReadAllBytes(path)); } }

We compare the normalized version of fileName with itself (the original input). If they differ, that means someone is trying to send us a manipulated fileName and we take appropriate action.

Normally the browser just sends the uploaded file name in its simplest form with no transformation.

For the sake of the argument, we may not even use the file name when the user uploads a file. We may be generating a GUID and use that instead.

Nevertheless, applying this control to the provided file name still matters, because hackers will definitely poke with that parameter no matter what.

Invalid Input Against Whitelists

Whitelisting is “accepting only what is expected”. In other words, if we come across some input that we do not expect, we reject it.

This input validation strategy is one of the most secure and effective strategies we have to this date. By using this strategy consistently throughout your software, you can close a lot of known and unknown venues that a malicious user can attack you.

This way of building a software is like building a closed castle with only thoroughly controlled doors opening outside, if that makes any sense.

OK, back to our topic.

Let’s analyze whitelisting with a simple scenario. Assume that our users have the freedom to choose their own, specific usernames when registering. And prior to coding, as a requirement we were informed how a username should look like.

Then, in order to comply with this requirement we can easily devise some rigid rules to apply against the username input before we accept it. If the input passes the test, we take in. Otherwise, we reject the input.

The whitelist rules may be in different forms, though. Some may contain a list of expected hard-coded values, others may check whether the input is integer or not. And others may be in the form of regular expressions.

Here is an example regular expression for usernames:

^[a-zA-Z0-9]{4,15}$

This regular expression is a very rigid whitelisting pattern. It matches with every string whose characters are nothing but a-z, A-Z, or 0-9. Not only this, but the length of the input should be minimum of 4 characters and maximum of 15 characters.

The hat at the beginning and dollar character at the end of the regular expression denote that the match should occur for the whole input.

Now assume that at runtime we get the following input which won’t pass our regular expression test:

o'neal

Does that mean our software is facing a hacker?

The input seems innocent. However, it might also be the case that a malicious user is just trying the existence of an SQL injection security bug before getting into the action, which is also known as reconnaissance.

Anyway, it’s still hard to deduce any malice from this particular case.

However, we can still seize the hackers using other forms of failed whitelists, such as failed input attempts against a list of expected hard-coded values.

An excellent example is JSON Web Token (JWT) standard. We use JWT when we want third parties to send us a claim that we can validate and then trust the data inside.

The standard has a simple JSON structure: a header, a body and a signature. The header contains how this particular claim should be produced and therefore validated. The body contains the claim itself. The signature is there for, well, validation.

For instance, when we get the following token from a third part, such as a user, we validate it using the algorithm it presents in the header value.

In this instance, the token itself tells us that we should use cryptographic hash HMACSHA256 algorithm (HS256 in the token is a short version) on both the header and body data to test whether it produces the same signature given.

If it produces the same signature value, then the token is authentic and we can trust the body:

// Header { "alg" : "HS256", "typ" : "JWT" } // Body { "userid": "[email protected]", "name": "John Doe", "iat": 1516239022 } // Signature AflcxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5g

There are various external libraries that we can easily use to produce and validate JWTs. Some of them had a serious security bug which let any JWT to be taken as an authentic token.

Here’s what went wrong with those libraries.

What happens when a token that we should validate contains a header like below? I just present the header here, but it also contains body and signature parts:

// Header { "alg" : "None", "typ" : "JWT" }

It seems that for that specific token some of those JWT validation libraries just accept the body as it is without any validation, because None says that no algorithm is applied for signature production.

To put this into perspective, that means any end user can send us any userid inside the token and we will not apply any validation against it and let them login.

The best way to avoid this and similar security problems is to keep a valid list of algorithms on our side. In this case the list may contain only one valid algorithm.

Moreover, it's better not to process the algorithm we get inside the header part of the JSON Web Token, whatever it might be.

But as you might have already guessed, there’s a huge opportunity here. We may just get the algorithm value from the header part and check even if we won’t use it. If the value is anything other than we expect, let’s say HS256, that means someone is messing around with us.

The same method can be used for any list of hard-coded values presented to the end user and one of which we expect to get as an input.

For example, if we provide a list of cities in a select box, we are sure that we will get back one of them when the form is posted. If we get a completely different value, there’s surely something wrong with the behavior of the user or automated tool we are facing.

Actions Against AuthN and AuthZ

One of the most critical parts of software from a security point of view are the authentication and the authorization mechanisms. These are places where we enforce that only the parties we know of access the application and they access certain parts within their roles.

In other words, our users shouldn’t use certain parts of our application without any credential validation and they shouldn’t access parts where they don’t have any privileges.

There are various attack scenarios against both of the mechanisms, however, the most obvious one against authentication is brute forcing. It is trying a set of pre-populated or generated on the fly credentials one after another in the hope that one or more of them would work.

Of course there are well-known ways to prevent such attacks: using CAPTCHAs or applying throttling on problematic IP addresses or usernames.

Usually authentication attacks are well-known and when noticed are already logged and possibly fed into the security monitoring systems.

The same is possible with attacks against authorization.

It’s easy to produce a security log and an alarm when our application returns an 403 response to our users. This well-known HTTP response is the indicator of an authorization problem, so it’s wise to log it.

However, both the authentication and the authorization cases so far have the potential to produce false alarms. However, I still encourage logging and producing alarms whenever these occur.

Now, let’s concentrate on a more solid case. Whenever we use Model-View-Controller (MVC) frameworks, we utilize the built-in auto-binding feature for our Action method parameters. So, the MVC framework we are using is in charge of binding parameters in HTTP requests onto our model objects automatically.

This is a great relief for us since getting each user input by using the low-level APIs of a framework really becomes tedious after some time.

What happens if this auto-binding becomes too permissive? Assume that we have a User model. It would probably have at least ten or twenty member fields. But for clarity, let’s say it has a FullName and a IsAdmin member fields.

The second member field will denote if a particular user is administrator or not:

public class User { public string FullName { get; set; } public bool IsAdmin { get; set; } }

In order for users to update their own profile, we prepare a View including the appropriate form and bindings.

At last, when the form is submitted, a controller action will auto-bind the HTTP parameters to a User class instance. Then, perhaps it will save it to the database just like below:

[HttpPost] public Result Update(User user) { UserRepository.Store(user); return View("Success"); }

Obviously here, a malicious non-administrative user may also set values of unwanted model members, such as IsAdmin. Since the binding is automatic, our malicious user can make themselves administrator by requesting a simple HTTP POST request to this action!

By using the MVC pattern, every model we use in action method parameters becomes fully visible and editable to end-users.

The best way to prevent this is using extra ViewModels or DTO objects for Views and Actions and include only the permitted fields. For example, here is a UserViewModel that only contains editable fields of User model class.

public class UserViewModel { public string FullName { get; set; } }

So, the end user, albeit she can add an additional IsAdmin parameter to the HTTP POST request, that value will not be used at all to result in a security problem. Excellent!

But wait, there’s a golden opportunity here to seize sophisticated hackers. How about we still include IsAdmin property in our UserViewModel, but produce a security log and maybe alarms when the setter is called:

public class UserViewModel { public string FullName { get; set; } public bool IsAdmin { set { // log, alarm, notify } } }

Just make sure that we don’t use this member field when we are creating a User model class instance out of this UserViewModel instance.

Miscellaneous

It is impossible to list or classify every possible case where we can place our little controls to notice any hacking attempts as early as possible. However, here are some of the other opportunities we have:

  • If our application provides a flow of actions which should be followed in a specific order, then any invalid order of calling indicates an abnormal behaviour.
  • Injection attacks are one of the most severe security bug categories that stem from insecure code and data concatenation. Cross Site Scripting (XSS), SQL Injection, and Directory Traversal are some common bugs in this category. Once we use secure constructs like contextual encodings, whitelist validation, and prepared statements, then we get rid of them. However, unfortunately, there are no simple and non-blacklist ways to seize the hackers who are still trying to abuse these security bugs once they are fixed.
  • La mise en place de pièges est également un moyen valable d'attraper les tentatives malveillantes, mais je suis contre cela si l'effort prend beaucoup de temps ou est susceptible de produire de fausses alarmes. Par exemple, il est possible d'inclure des liens masqués (affichage: aucun) dans nos pages Web et de déclencher la journalisation de la sécurité lorsque ces liens sont accessibles par des scanners de sécurité automatisés (car ils essaient d'accéder à tous les liens qu'ils peuvent extraire). Cependant, cela peut également générer de fausses alarmes pour les robots d'exploration légitimes, tels que Google. Pourtant, il s'agit d'un choix de conception et il existe de nombreux pièges qui peuvent être définis, tels que non existants mais faciles à deviner:
    • nom d'utilisateur, paires de mots de passe, par exemple le tristement célèbre administrateur: admin
    • chemins URL administratifs, par exemple / admin
    • En-têtes HTTP, paramètres, par exemple IsAdmin

Conclusion

«Le pardon n'approuve pas ce qui s'est passé. C'est choisir de s'élever au-dessus. » Robin Sharma

It is unforgivably naive to let malicious attempts towards our software go unnoticed while we already have the tools under our belt to do otherwise. Forgiveness is such a supreme moral quality, but we have to be on top of risky activities around our code.

Despite chaotic facets of software development, developing secure code is an important survival skill in this hacker-loaded world.

Moreover, we have the chance to improve this skill even further by noticing malicious activities in a precise manner in our code and producing security log entries and alarms for SOC teams.

Doing something about malicious behaviors in our code, like you read in this article is just one of the coding mistakes that lead to hacker abuse. I encourage you to check my Coding Mistakes that Hackers Abuse online training in order to master the rest of them.