Sådan fanges hackere i din kode

Hvad ville du gøre, hvis hackere misbruger din software i produktionen?

Dette er ikke et hypotetisk spørgsmål. De gør det sandsynligvis lige nu.

Du tænker måske over alle de sikre designvalg, du har foretaget, eller forebyggende teknikker, du har anvendt, så der er ikke noget at bekymre dig om.

I så fald er det godt - selvom der altid er ting, der overses, skal du altid tænke på dit systems sikkerhed.

Men der er en enorm forskel mellem at forhindre sikkerhedsfejl og tilgive ondsindede forsøg.

Hvad med at vi fanger og handler på de hackere, der prøver at bryde ind i vores software? I dette indlæg vil jeg forsøge at give dig praktiske og enkle eksempler på at fange typisk hackeradfærd i din kode tidligt.

Hvorfor fange ondsindede forsøg?

Er det ikke nok at forhindre sikkerhedsfejl? Jeg kan høre dig sige, ”Så længe jeg skriver sikker kode, er jeg ligeglad med, om hackere spiller med min bunnsolid software eller ej. Så hvorfor skal jeg bekymre mig om ondsindede forsøg? ”

Lad os først svare på dette gyldige spørgsmål.

Et noget komplekst stykke software er svært at holde sikkert hele tiden. Mere kompleksitet betyder flere potentielle svagheder for en hacker at misbruge, mens du designer, implementerer, implementerer eller vedligeholder koden.

Se bare på CVE-numrene gennem årene. Det er meget:

Desuden er en sikkerhedsfejl på grund af sin natur ikke bare en almindelig vare i din efterslæb. Der er nogle ubehagelige konsekvenser, hvis en sårbarhed bliver udnyttet: et tab af tillid, et dårligt omdømme eller endda økonomisk tab.

Så der findes best practices for sikkerhed såsom OWASP Application Security Verification Standard (ASVS) eller Mozillas retningslinjer for sikker kodning for at hjælpe udviklere med at producere sikker software.

Da der dog næsten dagligt opstår nye måder at omgå eksisterende sikkerhedskontrol eller nye svagheder, er der enighed omkring sikkerhedssamfundet om, at "Der er ingen 100% sikkerhed." Så vi skal altid være opmærksomme på og reagere på sikkerhedsnyheder og forbedringer.

Der er også en ting mere, vi kan gøre for at sikre sikker software: at bemærke hackere så tidligt som muligt, før de gør noget, som vi ikke forventer eller endda kender til. Desuden gør det os mere proaktive at holde styr på deres ondsindede opførsel over en lang periode.

Der er en populær forestilling om Security Operations Center (SOC) i denne retning - SOC'er er en type team i en organisation, der er outsourcet eller internt. Deres job er løbende at overvåge organisationens sikkerhedstilstand. De gør det ved at opdage, analysere og reagere på cybersikkerhedshændelser.

SOC-hold ser efter unormale aktiviteter, herunder anomalier i softwaresikkerhed. Ideen om at lægge mærke til og reagere på et vellykket eller mislykket cyberangreb giver organisationer overhånd over trusler, hvilket i sidste ende reducerer svartiden på angreb gennem kontinuerlig overvågning.

En SOC er kun stærk med det rige og kvalitetsinput, det får fra forskellige kilder til it-komponenter. Da vores software også er en vigtig del af lageret, er passende sikkerhedsalarmer på grund af unormal adfærd sendt af vores software til SOC-hold uvurderlige.

Sådan kontrolleres der for unormal adfærd

Her er et antal kontrol og kontroller, vi kan implementere i hele vores kode, der afslører ondsindet og unormal adfærd.

Inden vi begynder, vil jeg gerne understrege, at jeg ikke præsenterer komplicerede løsninger som Web Application Firewall (WAF) her. I stedet vil jeg bare prøve at vise dig, at enkle betingelser, smart undtagelseshåndtering og lignende lidt som ingen indsatshandlinger i din kode kan hjælpe dig med at bemærke unormal adfærd, så snart de opstår.

Lad os grave ind.

Nul længde eller nul returnerer

Den første handling, vi kan tage for at opdage en ondsindet handling, er ved at kontrollere aggregater med nul længde eller null returnerer.

Her er en simpel kodeblok for at illustrere pointen:

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

Her forsøger vi at få adgang til modtagelsen af ​​en bestemt overførsel leveret af vores slutbrugere via transferIdparameteren.

For at forhindre nogen i at få adgang til en andens kvitteringer, lad os antage, at inde i GetReceiptmetoden er vores udvikler smart nok til at kontrollere, om den transferIdvirkelig tilhører den nuværende bruger.

Kontrol af ejerskab er en god praksis for sikkerhed.

Lad os antage, at vi ved design er sikre på, at hver overførsel skal have mindst en relateret kvittering, så det er mistænkeligt at få ingen ved kørsel. Hvorfor? Fordi at få en tom kvittering betyder, at den leverede transferIdikke hører til nogen overførsel, der udføres af den nuværende bruger.

Med andre ord leverede den nuværende bruger en smedet transferIdtil vores kode og venter på at se indholdet, hvis det transferIdtilfældigvis er relateret til en andens transaktion.

Og da vi har den rette ejerskabskontrol, GetReceiptreturnerer metoden en tom eller nul kvittering. Det er her, vi skal tage nogle sikkerhedshandlinger.

Jeg vil ikke gå i detaljer med sikkerhedshandlingerne i dette indlæg. Dog er sikkerhedslogging og / eller afsendelse af detaljerede meddelelser, SIEM-systemer (Security Information and Event Management).

Her er et andet eksempel på, hvordan kontrol af nulværdien giver os mulighed for at gribe et ondsindet forsøg.

Tænk, at vi har følgende tre endepunkter, ShowReceipt, Success, og 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";

Dette er en simpel applikation, der viser indholdet af en brugers kvittering.

I ShowReceiptden første linje er en vigtig. Den kontrollerer, om slutbrugeren sender os en gyldig for receiptIdat se dens indhold. Uden denne kontrol kan en ondsindet bruger give enhver receiptIdog få adgang til indholdet.

Erklæringens plads i tredje linje er dog lige så vigtig. Hvis vi flytter denne linje lige før if-udsagnet, ville det ikke bryde noget. Det ville dog skabe det samme sikkerhedsproblem, som vi forsøgte at undgå ved at kontrollere, om slutbrugeren anmoder om en gyldig kvittering eller ej.

Brug et øjeblik for at sikre dig, at du forstår, hvorfor dette er tilfældet.

Nu er det en god idé, at vi placerede den linje på det rigtige sted, og det skaber en ny mulighed for at bemærke ondsindede forsøg. Så Successhvad betyder det i slutpunktet, hvis vi bliver nul receiptIdfra Session?

Det betyder, at nogen kalder dette slutpunkt, lige efter at de har anmodet om at ShowReceiptslutpunkt med en andens receiptId. Selvom de fik Erroromdirigering tilbage på grund af ejerskabskontrollen!

Naturligvis med den kontrol, vi har på første linje, er dette umuligt.

Successslutpunktet er et rart sted at skrive en sikkerhedslogpost og sende meddelelser til vores overvågningsløsninger, når vi får en null receiptIdfra Session.

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

Målrettet håndtering af undtagelser

Undtagelseshåndtering er måske den vigtigste mekanisme for udviklere til at reagere på enhver unormal tilstand under udførelsen af ​​programmet.

Det meste af tiden er den største mulighed, det giver, at rydde op i ressourcer, der blev lånt, såsom fil / netværksstrømme eller databaseforbindelser på uventede problemer. Dette er en fejlsikker opførsel, der lader os skrive mere pålidelige programmer.

Parallelt kan vi effektivt bruge runtime-undtagelser til at bemærke ondsindede forsøg på vores software.

Her er nogle populære svaghedskilder, hvor vi kan bruge relaterede undtagelser til at bemærke fiskeagtig adfærd:

  • Deserialisering
  • Kryptografi
  • XML-parsing
  • Almindelig udtryk
  • Aritmetiske operationer

Listen er selvfølgelig ikke komplet. Og her gennemgår jeg kun et par af disse API'er.

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.
  • Opsætning af fælder er også en gyldig måde at fange ondsindede forsøg på, men jeg er imod dette, hvis indsatsen tager meget tid eller sandsynligvis vil give falske alarmer. For eksempel er det muligt at medtage skjulte links (display: none) på vores websider og udløse sikkerhedslogging, når automatiske sikkerhedsscannere har adgang til disse links (fordi de prøver at få adgang til hvert link, som de kan udtrække). Dette kan dog også medføre falske alarmer for legitime crawlere, såsom Google. Alligevel er dette et designvalg, og der er mange fælder, der kan indstilles, såsom ikke-eksisterende, men let at gætte:
    • brugernavn, adgangskodepar, f.eks. den berygtede admin: admin
    • administrative URL-stier, f.eks. / admin
    • HTTP-overskrifter, parametre, f.eks. IsAdmin

Konklusion

”Tilgivelse godkender ikke, hvad der skete. Det vælger at hæve sig over det. ” Robin Sharma

Det er utilgiveligt naivt at lade ondsindede forsøg på vores software gå ubemærket hen, mens vi allerede har værktøjerne bag os til at gøre andet. Tilgivelse er sådan en yderst moralsk kvalitet, men vi skal være på toppen af ​​risikable aktiviteter omkring vores kode.

På trods af kaotiske aspekter af softwareudvikling er udvikling af sikker kode en vigtig overlevelsesfærdighed i denne hacker-belastede verden.

Desuden har vi chancen for at forbedre denne færdighed yderligere ved at bemærke ondsindede aktiviteter på en præcis måde i vores kode og producere sikkerhedslogposter og alarmer til SOC-hold.

At gøre noget ved ondsindet adfærd i vores kode, som du læser i denne artikel, er kun en af ​​kodningsfejlene, der fører til hackermisbrug. Jeg opfordrer dig til at kontrollere mine kodningsfejl, som hackere misbruger online-træning for at mestre resten af ​​dem.