Currently, many people are talking about ChatGPT and how it will affect everybody’s life. Some (including me) are also investigating it. Therefore, let’s put it to the test. Does ChatGPT know how to find the right and relevant information to design and implement a use case around a password spray attack? Put differently, can ChatGPT replace the use case developer?
The basics first. What is a password spray attack? This a simple but good question for ChatGPT.
Q: What is a password spray attack?
A: A password spray attack is a type of cyber attack that targets
authentication systems, such as login pages or remote access services,
in order to gain unauthorized access to user accounts. Unlike traditional
brute force attacks, which involve trying a large number of passwords against
a single user account, password spray attacks work by attempting a small
number of commonly used passwords against a large number of user accounts.
The attack derives its name from the analogy of "spraying" a large number of
user accounts with a limited set of passwords, hoping that some of the accounts
have weak or commonly used passwords. Instead of targeting a specific user,
the attacker tries a few frequently used passwords (e.g., "123456" or
"password") across many user accounts.
Password spray attacks take advantage of the fact that many users tend to
choose weak passwords or reuse passwords across multiple accounts. By trying
commonly used passwords against a large number of accounts, attackers increase
their chances of successfully compromising at least a few accounts.
The first clues on how to detect a password spray attack are there. A high number of users are involved. However, the variety of used passwords is low. And often these passwords are commonly used passwords. Now, a little more challenging question.
Q: How can you detect a password spray attack?
A: Detecting a password spray attack can be challenging since the attack
typically involves a small number of attempts against each user account,
making it less likely to trigger conventional intrusion detection systems (IDS)
or account lockouts. However, there are several strategies and techniques that
can help in detecting password spray attacks:
Account Lockouts and Failed Login Monitoring: Keep an eye on account lockouts
or an unusually high number of failed login attempts. While a single failed
attempt may not be significant, a sudden increase in failed login attempts
across multiple accounts can indicate a password spray attack.
Anomaly Detection: Implement anomaly detection mechanisms that can identify
unusual patterns in authentication attempts. Look for patterns like multiple
login attempts from different IP addresses or geographically distant locations
within a short time frame, which may indicate an ongoing password spray attack.
User and Account Behavior Monitoring: Monitor user behavior and account
activity. Identify any abnormal behavior, such as sudden login attempts from
unused or dormant accounts, as this could indicate an attacker testing a list
of compromised credentials obtained from other sources.
Password Complexity and Reuse Analysis: Regularly analyze password complexity
and reuse within your organization. Identify accounts with weak or commonly
used passwords that are more likely to be targeted in password spray attacks.
Log Analysis and Event Correlation: Analyze logs from various systems,
including authentication servers, firewalls, and intrusion detection systems,
to identify patterns that indicate a password spray attack. Look for multiple
failed login attempts with the same source IP address, sequential or patterned
login attempts, or a high number of failed logins against multiple accounts.
Threat Intelligence: Stay updated with the latest threat intelligence and known
attacker techniques. Share information with security communities or subscribe
to threat intelligence feeds to receive alerts about ongoing password spray
attacks targeting specific industries or organizations.
User Awareness and Education: Educate users about the risks of weak passwords
and the importance of using strong, unique passwords. Encourage them to enable
multi-factor authentication (MFA) for their accounts
‘Account Lockouts and Failed Login Monitoring’. This is a difficult one. First of all, the provided password is not saved in the event data. Therefore, it would be challenging to differentiate between a brute-force attack and a password spray attack as both pivot around failed logins. You could say a high variety of usernames from the same source IP is pointing towards a password spray attack, while a high number of the same username is pointing towards a brute-force attack. The issue with this is that it is all relative.
‘Anomaly Detection’. This is an odd one because this is hinting towards the use case Impossible Travel and not so much a password spray attack. However, impossible travel can be an indicator that the account is part of a larger attack pattern. ‘User and Account Behavior Monitoring’. Similar to the previous one. It is not directly related to a password spray attack. ‘Log Analysis and Event Correlation’. In other words, SIEM. This is where you could perform the detection of a password spray attack.
‘Password Complexity and Reuse Analysis’. Although this is good advice. It is not related to the question ‘How to detect a password spray attack?’. It is answering the question of which accounts are most likely to be targeted during a password spray attack. ‘Threat Intelligence’ and ‘User Awareness and Education’. This is just way too generic. It does not provide any clues on how to detect a password spray attack.
Let’s see what ChatGPT is saying when I zoom in on the password element in the logging.
Q: Can you detect a password spray attack if in the logging nothing is
mentioned about the password itself?
A: If the logging does not include the actual password used in the
authentication attempts, detecting a password spray attack becomes more
challenging. However, there are still some indicators and techniques that can
help in identifying such attacks:
Failed Login Attempts: Monitoring the frequency and pattern of failed login
attempts can provide valuable insights. Look for a high number of failed login
attempts across multiple accounts coming from a single or a few IP addresses.
Even without knowing the passwords, detecting a sudden increase in failed login
attempts can indicate a password spray attack.
User Account Lockouts: Keep an eye on user account lockouts. If multiple user
accounts are being locked out within a short period, it may suggest that an
attacker is systematically attempting to guess passwords across a range of
accounts.
Unusual Login Times and Locations: Analyze login times and locations. If there
are numerous login attempts from different locations within a short time frame,
it can indicate an ongoing password spray attack. While you may not know the
specific passwords, the unusual login patterns can be indicative of malicious
activity.
User Behavior Analytics: Implement user behavior analytics to detect anomalies
in user activities. Look for deviations from normal behavior, such as unusual
login times, login attempts from unfamiliar locations, or multiple failed login
attempts from a single user. These abnormal patterns can indicate a password
spray attack, even without knowing the actual passwords.
Rate Limiting and CAPTCHA: Implement rate limiting mechanisms and CAPTCHA
challenges to deter automated password spraying. Limiting the number of login attempts from a specific IP address or implementing CAPTCHA after a certain number of failed attempts can make password spraying more difficult for attackers.
Threat Intelligence: Stay updated with the latest threat intelligence and known
attacker techniques. Monitor security community forums, threat intelligence
feeds, or engage in information sharing to be aware of ongoing password spray
attacks targeting specific industries or organizations.
While not having access to the actual passwords used in the attack makes
detection more challenging, a combination of the above techniques, along with
proactive monitoring and analysis, can help identify suspicious patterns and
activities associated with password spray attacks.
Quite an extensive answer while a simple yes or no would have been enough. And again, ChatGPT is deviating from the question. But at least, the definition of a password spray attack is defined.
Now, let’s move to the technology layer and ask ChatGPT which tools you need to detect a password spray attack.
Q: Which tool(s) do you need to detect a password spray attack?
A: Detecting a password spray attack typically involves a combination of tools
and techniques. Here are some tools commonly used in detecting and mitigating
password spray attacks:
Security Information and Event Management (SIEM) Systems: SIEM solutions
aggregate and analyze logs from various systems and applications within an
organization's network. They help in monitoring and correlating events,
allowing you to identify patterns associated with password spray attacks. SIEM
tools can also generate alerts and reports to facilitate incident response.
Intrusion Detection and Prevention Systems (IDS/IPS): IDS/IPS systems monitor
network traffic for suspicious activities and known attack patterns. They can
detect and alert on multiple failed login attempts from specific IP addresses
or unusual login behavior, providing an early warning of possible password
spray attacks.
User and Entity Behavior Analytics (UEBA) Tools: UEBA tools analyze user
behavior patterns and establish baselines for normal behavior. By detecting
deviations from these baselines, UEBA tools can identify anomalous activities,
such as unusual login times, locations, or failed login attempts across
multiple accounts, which can indicate a password spray attack.
Web Application Firewalls (WAF): WAF solutions can detect and mitigate certain
types of password spray attacks targeting web applications. They can identify
patterns of suspicious login attempts, such as multiple failed login attempts
from a single IP address, and apply mitigation measures, such as IP blocking or
CAPTCHA challenges.
Log Management and Analysis Tools: Log management tools facilitate the
collection, storage, and analysis of logs from various systems and applications.
These tools enable you to search for specific events, correlate data, and
identify patterns associated with password spray attacks.
Threat Intelligence Platforms: Threat intelligence platforms provide up-to-date
information on known threats, attack techniques, and indicators of compromise (IOCs). They can help in identifying ongoing password spray attacks by providing context and alerts based on the latest threat intelligence.
Active Directory (AD) Security Tools: Tools specifically designed for Active
Directory environments can help monitor and analyze authentication attempts.
They can identify patterns of failed logins, account lockouts, or suspicious
login activities across user accounts, which may indicate a password spray
attack.
It's important to note that the specific tools needed may vary based on your
organization's infrastructure, security requirements, and budget. A
combination of the above tools, along with strong security practices, regular
monitoring, and user education, can help in effectively detecting and
mitigating password spray attacks.
Hmm, till now nothing is mentioned about encrypted traffic. Let’s rephrase the question and include this in the question.
Q: Which tool(s) do you need to detect a password spray attack if the network
traffic is encrypted?
A: If network traffic is encrypted, it can pose challenges for detecting a
password spray attack as the payload, including the actual login attempts, is
not visible in plain text. However, there are still tools and techniques that
can be used to detect password spray attacks in encrypted network environments:
Intrusion Detection and Prevention Systems (IDS/IPS): While encrypted traffic
cannot be inspected directly, IDS/IPS systems can still analyze network
metadata and behavior to detect patterns associated with password spray
attacks. They can monitor factors such as the number of connections, packet
sizes, communication patterns, and anomalies in encrypted traffic flows.
Endpoint Detection and Response (EDR) Tools: EDR solutions can monitor
activities on endpoints, such as workstations or servers, to detect signs of a
password spray attack. EDR tools can analyze authentication logs, failed login
attempts, or unusual login patterns at the endpoint level, providing insights
into potential password spray attacks.
Network Traffic Analysis (NTA) Tools: NTA tools can analyze network traffic
patterns and anomalies even when encrypted. They utilize techniques such as
behavioral analysis, machine learning, and statistical modeling to identify
suspicious activities. NTA tools can detect unusual login behavior, multiple
failed login attempts, or patterns of login activities that indicate a password
spray attack.
Log Analysis and Event Correlation: Log analysis tools can examine system logs,
authentication logs, and other relevant logs for patterns associated with
password spray attacks. While the actual passwords may not be visible,
analyzing other log data such as failed login attempts, IP addresses,
timestamps, and user account information can help identify potential password
spray attacks.
User and Entity Behavior Analytics (UEBA): UEBA tools monitor user behavior
and can identify anomalies even in encrypted traffic. By establishing baselines
of normal behavior and comparing it to current activities, UEBA can detect
patterns like unusual login times, locations, or failed login attempts across
multiple accounts, which may indicate a password spray attack.
Security Information and Event Management (SIEM) Systems: SIEM solutions can
collect and correlate logs from various sources, including authentication
systems, endpoints, and network devices. They can analyze events and identify
potential password spray attacks by correlating information such as failed
login attempts, user activities, and network traffic metadata.
It's important to note that detecting password spray attacks in encrypted
traffic may require a combination of these tools and techniques, as well as
proper configuration and monitoring of network infrastructure and security
systems. Additionally, implementing measures such as endpoint protection,
strong authentication mechanisms, and user education can help mitigate the
risk of password spray attacks in encrypted environments.
Interestingly to see the ‘Web Application Firewalls (WAF)’ and ‘Active Directory (AD) Security Tools’ are no longer mentioned. Therefore, you could derive the incorrect conclusion these options are only applicable if you have unencrypted network traffic.
One of the common elements between both answers, is IDS/IPS. Is there really an IDS/IPS rule that can detect a password spray attack? That would be awesome.
Q: What is the IDS/IPS signature to detect a password spray attack?
A: Specific signatures to detect password spray attacks can vary depending
on the IDS/IPS solution and the configuration in use. However, here's an
example of a general signature that can help detect patterns associated
with password spray attacks:
Signature Name:
Suspicious Failed Login Attempts
Rule:
alert tcp $EXTERNAL_NET any -> $HOME_NET any (msg:"Suspicious Failed Login
Attempts - Possible Password Spray Attack"; flow:established,from_server;
content:"Invalid user"; nocase; content:"Password authentication failed";
nocase; threshold:type limit, track by_src, count 5, seconds 60; sid:1000001;
rev:1;)
Explanation:
This signature is designed to detect multiple failed login attempts with the
phrases "Invalid user" and "Password authentication failed" occurring within
a short time frame. The threshold directive sets the criteria for triggering
the alert. In this example, the rule triggers an alert if there are five or
more failed login attempts from the same source IP address within 60 seconds.
Please note that this is just an example signature, and it may require
customization based on your specific network environment, logging capabilities,
and threat landscape. It's recommended to consult the documentation and
guidelines provided by your IDS/IPS vendor for creating or fine-tuning
signatures to detect password spray attacks effectively.
Hmm, ChatGPT is kind of missing the boat I would say. It is not differentiating between a brute-force attack and a password spray attack. It is also missing the boat because again it is not saying anything about encrypted network traffic.
But it gets worse. It depends on the destination to report the specific string ‘invalid user’. But when you read the detailed description of Microsoft Windows Event ID 4625, only by examining the sub-status code, you can understand why the login attempt is failing.
When you think logically, the only place to detect a password spray attack is the place where the credentials are validated. For example, at the domain controller. That means only ‘Endpoint Detection and Response (EDR) Tools’ and ‘Active Directory (AD) Security Tools’ are good suggestions by ChatGPT.
The suggestion ‘Web Application Firewalls (WAF)’ might also be an option if the network traffic is stopped and started on the WAF solution. However, not many WAF solutions do support detecting a password spray attack.
‘Active Directory (AD) Security Tools’ — Microsoft does offer password spray detection. But not straight out of the box. You do need to set it up first. A small detail that ChatGPT forgot to mention.
So okay, with a bit of luck, you can detect a password spray attack. But what are the investigation steps you need to do?
Q: How can you investigate a password spray attack?
A: Investigating a password spray attack involves a systematic approach to
gather evidence, analyze the attack, and determine the extent of the compromise.
Here are steps you can follow to investigate a password spray attack:
Gather Information: Collect as much information as possible about the attack.
This includes timestamps of suspicious activities, affected user accounts, IP
addresses involved, relevant logs, and any available network traffic captures.
Consult authentication logs, system logs, firewall logs, IDS/IPS alerts, and
any other relevant sources.
Identify Patterns: Look for patterns or anomalies in the data. Focus on failed
login attempts, account lockouts, and unusual login activities across multiple
accounts. Analyze timestamps, source IP addresses, login patterns, and any
other relevant data to identify consistent patterns associated with the attack.
Determine Scope: Assess the extent of the attack. Identify the number of
compromised accounts, potential data breaches, and any unauthorized access or
activities that might have occurred. Determine whether the attack was limited
to password spraying or if there are indications of further exploitation or
lateral movement within the network.
Preserve Evidence: Preserve all relevant evidence for further analysis and
potential legal actions. Maintain logs, network captures, and any other
forensic evidence in a secure and tamper-proof manner. Ensure the chain of
custody is maintained to ensure the integrity of the evidence.
Analyze Logs and Traffic: Thoroughly examine the collected logs and network
traffic captures. Look for indicators of the password spray attack, such as
repetitive failed login attempts from specific IP addresses, unusual login
patterns, or unexpected login locations. Correlate this information with
timestamps, user accounts, and any available contextual data to reconstruct
the attack timeline.
Determine Attack Vectors: Identify the attack vectors used by the attacker.
Determine whether the attack was targeted towards specific applications,
services, or systems. Look for any vulnerabilities or misconfigurations that
allowed the password spray attack to succeed.
Assess Compromised Accounts: Investigate the compromised user accounts. Analyze
the extent of the unauthorized access and any potential data exfiltration or
unauthorized activities that occurred. Review logs, account settings, and
activity histories to gather evidence and understand the impact on the affected
accounts.
Mitigation and Recovery: Once the investigation is complete, take steps to
mitigate the attack's impact and prevent future occurrences. This may include
resetting compromised passwords, implementing stronger authentication measures
like multi-factor authentication (MFA), addressing any vulnerabilities or
misconfigurations, and educating users to prevent future password spray
attacks.
Report and Incident Response: Document the findings of the investigation in a
detailed report. Include information on the attack timeline, compromised
accounts, impact assessment, and recommended mitigation measures. Notify
appropriate stakeholders, such as IT teams, management, and potentially legal
or regulatory entities if necessary.
During the investigation, it's important to maintain proper documentation,
adhere to legal and regulatory requirements, and involve the appropriate
incident response and forensic experts if needed. Timely and thorough
investigation can aid in understanding the attack, mitigating risks, and
preventing similar incidents in the future.
That is an extremely generic incident response approach. Nothing specifically mentioned around a password spray attack. ChatGPT again missed the boat.
If ChatGPT is going to take over the role as a use case developer, it needs to be able to implement the use case in the existing SIEM solution. The SIGMA language is an industry-standard to ensure you can quickly implement use cases no matter which SIEM platform is used.
Q: What is the SIGMA definition to detect a password spray attack?
A: SIGMA is a specification language used to create and share detection rules
for security monitoring and threat detection systems. While there is no
standard or official SIGMA definition specifically for detecting password spray
attacks, you can create a custom SIGMA rule to detect patterns associated with
password spray attacks.
Here's an example of a SIGMA rule that can help in detecting password spray
attacks:
<yaml code>
title: Suspicious Failed Login Attempts - Possible Password Spray Attack
description: Detects multiple failed login attempts from the same source IP address within a short time period.
author: Your Name
logsource:
category: authentication
detection:
selection:
event.action: 'authentication_failed'
condition: >
count() by event.src_ip > 5 AND timeWindow() < 60s
fields:
- event.src_ip
output:
- event.action
- event.src_ip
Explanation:
This SIGMA rule is designed to detect multiple failed login attempts from the
same source IP address within a 60-second time window. It filters events where
the event.action field is set to "authentication_failed" and then checks if
there are more than 5 events (failed login attempts) from a single source IP
address within the defined time window. If the condition is met, the rule
outputs the event.action and event.src_ip fields for further investigation.
It's important to note that the actual implementation of SIGMA rules may
depend on the specific SIEM or threat detection system being used. The above
example is a basic illustration and may require customization or adjustment
based on your environment and the log format of your security monitoring system.
You can adapt and enhance this example to match your specific log source,
logging format, and detection requirements.
It is based on a similar approach as the IDS/IPS signature, but instead of specifically looking for the words ‘Invalid user’, it looks for all events where the action ‘Authentication failure’. But this does raise a dependency. All the events must be properly tagged in the SIEM solution. And same as with the IDS/IPS rule, it does not make the differentiation between a brute-force attack and a password spray attack.
Conclusion:
Based on the answers of ChatGPT, I would say ChatGPT is not yet ready to take over the role of a use case developer. The provided answers are too generic and are missing in some cases crucial information. But ChatGPT is not completely useless for a use case developer. It does provide enough information to provide a basic understanding of what the use case is all about. It is then up to the use case developer to interrupt all the information. It is my personal expectation that one-day ChatGPT will have the capability to take over some roles in our daily lives.
Disclaimer:
This article is based on the output provided by OpenAI on May 22, 2023. The model used during writing this article is text-davinci-002-render-sha.
Leave a Reply