In this lesson,
we will learn about Malicious Code Attacks.
Malicious code attacks
are the insertion of harmful software or code into a system
to disrupt, steal, or manipulate data and operations.
Malicious code attack concepts include
implants and poisoning.
Implants are malicious code or hardware
inserted into a system, often stealthily.
Implants may be used to create a persistent backdoor
that attackers can use for ongoing access and control
to a machine.
Next, poisoning occurs
when attackers tamper with the data or environment
used by a system to manipulate its behavior.
Let's learn more about implants and poisoning.
First, we have implants.
Implants are a type of malicious code attack
where an attacker inserts a persistent back door
or unauthorized code into a system allowing continuous,
often stealthy access.
Implants can take the form of software, firmware,
or even hardware that hides within a system
to provide attackers with a remote connection and control.
Implants are particularly dangerous because once implanted,
they may remain undetected for extended periods,
giving attackers repeated access to sensitive information
or the ability to manipulate the system
over a long period of time.
The use of implants is common in advanced persistent threat
tactics, techniques, and procedures.
A software implant could be a malicious script
hidden within a legitimate application.
For example, imagine a scenario
where an attacker gains access to a server
and injects code that creates a hidden back door,
allowing remote access every time the application runs.
A simple Python implant might look like this.
This is a code snippet not the entirety of the code
that would be processed.
In the example on the screen,
the implant_backdoor function
collects environmental variables,
which could include credentials
and sends them via a post request
to the myattackersite.com
every time the process_data function is called
making the implant difficult to detect
because it's embedded in the applications flow.
So to mitigate implant vulnerabilities,
organizations should employ rigorous code reviews,
especially when dealing with code
from external or unverified sources.
Additionally, endpoint monitoring tools such as OSSEC,
CrowdStrike and Carbon Black should be employed
to detect unusual system behavior.
Finally, regular integrity checks on critical files
and configurations should be conducted
to uncover unauthorized and malicious code modifications.
Second, we have poisoning.
Poisoning is a malicious attack
where an attacker manipulates
the data or environment of a system to alter its behavior
or produce incorrect outcomes.
Poisoning often targets data-driven systems such as
machine learning models or databases
and feeds them manipulated or misleading data.
This type of attack can degrade the system's accuracy
or cause it to behave unpredictably,
which is particularly harmful in applications
where accuracy and reliability are particularly important,
like in security software or financial models.
An example of poisoning occurs in machine learning
where an attacker intentionally introduces malicious data
into a model's training set.
For instance, consider an email filter,
trained to detect spam based on a data set of email samples.
If the attacker gains access
and injects benign looking spam emails
into the training data,
the model could learn to become less effective
at identifying spam.
In code a simplified poisoning script snippet
might look like this.
In the example on the screen, the email,
"Discount offer just for you!"
Is mislabeled as ham,
which is a term used for legitimate nonspam emails.
This mislabeling is intentional
and designed to confuse the model
by training it to misclassify similar spam messages
as legitimate.
Over time if enough misleading labels like this
are introduced, the model's accuracy will degrade,
making it more likely to allow spam messages
into users inboxes.
Poisoning attacks like this can be particularly damaging
in real world applications
as they can alter the model's behavior in subtle ways
that accumulate and result in significant vulnerabilities.
Such attacks may lead to poor performance in spam detection,
allowing harmful or fraudulent messages to bypass filters
and reach end users.
So to protect against poisoning,
it's essential to control access to training data,
ensuring only trusted sources can modify it.
Next, employing data validation checks
can also help detect abnormal patterns in the data.
Finally, monitoring the model's performance
for sudden shifts or accuracy drops
can indicate possible poisoning.
And retraining the models with clean, validated data
helps maintain system integrity.
So remember, malicious code attacks
happen when harmful software or code
is inserted into a system to disrupt, steal,
or manipulate data processes.
Two main types of these attacks are implants and poisoning.
Implants are stealthy back doors,
often embedded into software, firmware or hardware
giving attackers continuous access and control
over a system.
Poisoning on the other hand
involves tampering with the data or environment
a system relies on often causing it to make errors
or behave unpredictably.
Both implants and poisoning attacks can be damaging,
so strong security measures, including monitoring
and data validation can help prevent these types of attacks.