Catching Mimikatz Behavior with Anomaly Detection Defense
14
Feb 2019
14
Feb 2019
Detect and prevent Mimikatz cyber attacks with AI anomaly detection. Read more to learn how cyber defenses can stop Mimikatz and its variants in their tracks.
Originally created by famed French programmer Benjamin Delpy to highlight security flaws in Windows authentication mechanisms, today Mimikatz is a staple post-exploitation module in the arsenal of cyber-criminals, since it facilitates lateral movement across a victim’s network. Mimikatz was a primary feature of the global ransomware attacks NotPetya and BadRabbit, in addition to the alleged Russian hacking of the German parliament in 2015 and 2017.
Among the primary vulnerabilities that Mimikatz exploits is Windows’ Local Security Authority Subsystem Service (LSASS), which is designed to obviate the need for users to reauthenticate every time they seek to access internal resources. Despite its clear utility, LSASS works by keeping a cache of every credential used since the last boot, presenting an obvious security risk in the event the cache is compromised. Broadly speaking, Mimikatz plunders this resource and allows users to access cleartext passwords as well as NTLM hashes. With this data in hand, threat actors are able to conduct the following attacks:
Kerberos Golden Ticket: Provides administrative credentials for the whole domain.
Pass-the-Ticket: Enables a user to pass a Kerberos ticket to a second device and login using this ticket.
Kerberos Silver Ticket: Provides a TGS ticket to log into any network service.
Pass-the-Hash: Allows a user to pass a hash string in order to login.
Dumping LSASS memory is just one method that Mimikatz and its many updated versions employ to harvest credentials. Indeed, once malware such as NotPetya has established itself on single device, the Mimikatz module can exploit a variety of security flaws to obtain the password information for any other users or computers that have logged onto that machine: a key step for both lateral movement and privilege escalation. Like many successful hacker tools, Mimikatz has inspired the creation of other programs with similar aims, which are largely intended to circumvent antivirus controls.
Using AI to end the cat-and-mouse game
At the most basic level, security teams can reduce vulnerability to Mimikatz and to lateral movement more generally by ensuring that each user has the minimum amount of privileges needed for his or her role. But while this measure is certainly prudent, it will not always be effective — especially when dealing with sophisticated threats. Another strategy is to implement endpoint security tools and anti-virus software, which rely on rules and signatures to detect known Mimikatz variants. However, as Mimikatz and its copycats continue to evolve, these traditional tools are locked in a ceaseless cat-and-mouse game, unable to spot unknown variants of Mimikatz that are designed to circumvent them.
As a fundamentally different approach to security, artificially intelligent systems like Darktrace avoid this cat-and-mouse game by learning what constitutes normal behavior for the users and devices they safeguard while “on the job,” rather than using fixed rules and signatures. This approach alerts defenders to any anomalous activity, regardless of whether such activity constitutes a known or unknown threat. Typically, lateral movement involving Mimikatz will involve a spike in unusual SMB activity, as attackers seek to write the tool to target devices. The following recent attack on a Darktrace customer highlights how AI can allow security teams to quickly detect and respond to such lateral movement involving Mimikatz:
Darktrace alerts to a non-domain joined Linux device appearing on the customer’s network and engaging in extensive SMB bruteforce activity against key servers.
Figure 1: Darktrace detects the spike in SMB activity.
Darktrace flags the device successfully logging into a server using administrative credentials via SMBv1, opening the \sect pipe and writing the suspicious and likely malicious file Syssvc.exe to the server’s \temp folder. Immediately after this, Darktrace alerts to the device writing mimikatz.exe to the same folder.
Figure 2: Darktrace flags the device using Mimikatz.
Following this step, multiple .tmp and .txt files appear on the target device, indicating that Mimikatz was proceeding to access passwords and hashes.
The novelty of this AI-powered approach is that it does not rely on a string search for the term “mimikatz.exe”; rather, it highlights the unusual behavior that commonly surrounds Mimikatz’ activity. As shown in the screenshot above, this behavior constitutes “New activity”: Darktrace has not seen this type of SMB activity between the source and the destination before. Moreover, SMBv1 is used — highly unusual for the environment. And finally, it is unusual for devices in the target environment to make SMB network drive writes to Temp folders. All these subtle deviations from the customer’s ‘pattern of life,’ taken together, caused Darktrace to alert on the Mimikatz behavior.
Since its introduction to the cyber-threat landscape, Mimikatz has become a highly effective means for cyber-attackers to move laterally inside corporate and government networks. But by empowering security teams to respond before attackers can plunder a network’s entire cache of passwords, AI cyber defenses are thwarting Mimikatz and its copycats alike.
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
AUTHOR
ABOUT ThE AUTHOR
Max Heinemeyer
Chief Product Officer
Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.
What began as a method to achieve unauthorized access to an account, often driven by the curiosity of individual attackers, credentials theft become a key tactic for malicious actors and groups, as stolen login credentials can be abused to gain unauthorized access to accounts and systems. This access can be leveraged to carry out malicious activities such as data exfiltration, fraud, espionage and malware deployment.
It is therefore no surprise that the number of dark web marketplaces selling privileged credentials has increased in recent years, making it easier for malicious actors to monetize stolen credentials [1]. This, in turn, has created new opportunities for threat actors to use increasingly sophisticated tactics such as phishing, social engineering and credential stuffing in their attacks, targeting individuals, organizations and government entities alike [1].
Credential theft example
TA577 Threat Actor
TA577 is a threat actor known to leverage stolen credentials, also known as Hive0118 [2], an initial access broker (IAB) group that was previously known for delivering malicious payloads [2]. On March 4, 2024, Proofpoint reported evidence of TA577 using a new attack chain with a different aim in mind: stealing NT LAN Manager (NTLM) hashes that can be used to authenticate to systems without needing to know plaintext passwords [3].
How does TA577 steal credentials?
Proofpoint reported that this new attack chain, which was first observed on February 26 and 27, was made up of two distinct campaigns. The first campaign consisted of a phishing attack featuring tens of thousands of emails targeting hundreds of organizations globally [3]. These phishing emails often appeared as replies to previous messages (thread hijacking) and contained zipped HTML attachments that each contained a unique file hash, customized for each recipient [3]. These attached files also contained a HTTP Meta refresh function, which triggered an automatic connection to a text file hosted on external IP addresses running as SMB servers [3].
When attempting to access the text file, the server requires an SMB session authentication via NTLM. This session is initiated when a client sends an ‘SMB_COM_NEGOTIATE’ request to the server, which answers with a ‘SMB_COM_NEGOTIATE’ response.
The client then proceeds to send a ‘SMB_COM_SESSION_SETUP_ANDX’ request to start the SMB session setup process, which includes initiating the NTLM authentication process. The server responds with an ‘SMB_COM_SESSION_SETUP_ANDX’ response, which includes an NTLM challenge message [6].
The client can then use the challenge message and its own credentials to generate a response by hashing its password using an NTLM hash algorithm. The response is sent to the server in an ‘SMB_COM_SESSION_SETUP_ANDX’ request. The server validates the response and, if the authentication is successful, the server answers with a final ‘SMB_COM_SESSION_SETUP_ANDX’ response, which completes the session setup process and allows the client to access the file listed on the server [6].
What is the goal of threat actor TA577?
As no malware delivery was detected during these sessions, researchers have suggested that the aim of TA577 was not to deliver malware, but rather to take advantage of the NTLMV2 challenge/response to steal NTLM authentication hashes [3] [4]. Hashes stolen by attackers can be exploited in pass-the-hash attacks to authenticate to a remote server or service [4]. They can also be used for offline password cracking which, if successful, could be utilized to escalate privileges or perform lateral movement through a target network [4]. Under certain circumstances, these hashes could also permit malicious actors to hijack accounts, access sensitive information and evade security products [4].
The open-source toolkit Impacket, which includes modules for password cracking [5] and which can be identified by the default NTLM server challenge “aaaaaaaaaaaaaaaa”[3], was observed during the SMB sessions. This indicates that TA577 actor aim to use stolen credentials for password cracking and pass-the-hash attacks.
TA577 has previously been associated with Black Basta ransomware infections and Qbot, and has been observed delivering various payloads including IcedID, SystemBC, SmokeLoader, Ursnif, and Cobalt Strike [2].This change in tactic to follow the current trend of credential theft may indicate that not only are TA577 actors aware of which methods are most effective in the current threat landscape, but they also have monetary and time resources needed to create new methods to bypass existing detection tools [3].
Darktrace’s Coverage of TA577 Activity
On February 26 and 26, coinciding with the campaign activity reported by Proofpoint, Darktrace/Email™ observed a surge of inbound emails from numerous suspicious domains targeting multiple customer environments. These emails consistently included zip files with seemingly randomly generated names, containing HTLM content and links to an unusual external IP address [3].
Figure 1: A summary of anomaly indicators seen for a campaign email sent by TA577, as detected by Darktrace/Email.
Figure 2: Details of the name and size of the .zip file attached to a campaign email, along with the Darktrace/Email model alerts triggered by the email.
The URL of these links contained an unusually named .txt file, which corresponds with Proofpoint reports of the automatic connection to a text file hosted on an external SMB server made when the attachment is opened [3].
Figure 3: A link to a rare external IP address seen within a campaign email, containing an unusually named .txt file.
Darktrace identified devices on multiple customer networks connecting to external SMB servers via the SMB protocol. It understood this activity was suspicious as the SMB protocol is typically reserved for internal connections and the endpoint in question had never previously been observed on the network.
Figure 4: The Event Log of a ‘Compliance / External Windows Communication’ model alert showing a connection to an external SMB server on destination port 445.
Figure 5: External Sites Summary highlighting the rarity of the external SMB server.
Figure 6: External Sites Summary highlightin that the SMB server is geolocated in Moldova.
During these connections, Darktrace observed multiple devices establishing an SMB session to this server via a NTLM challenge/response, representing the potential theft of the credentials used in this session. During this session, some devices also attempted to access an unusually named .txt file, further indicating that the affected devices were trying to access the .txt file hosted on external SMB servers [3].
Packet captures (PCAPs) of these sessions show the default NTLM server challenge, indicating the use of Impacket, suggesting that the captured NTLM hashes were to be used for password cracking or pass-the-hash-attacks [3]
Figure 7: PCAP analysis showing usage of the default NTLM server challenge associated with Impacket.
Conclusions
Ultimately, Darktrace’s suite of products effectively detected and alerted for multiple aspects of the TA577 attack chain and NTLM hash data theft activity across its customer base. Darktrace/Email was able to uncover the inbound phishing emails that served as the initial access vector for TA577 actors, while Darktrace DETECT identified the subsequent external connections to unusual external locations and suspicious SMB sessions.
Furthermore, Darktrace’s anomaly-based approach enabled it to detect suspicious TA577 activity across the customer base on February 26 and 27, prior to Proofpoint’s report on their new attack chain. This showcases Darktrace’s ability to identify emerging threats based on the subtle deviations in a compromised device’s behavior, rather than relying on a static list of indicators of compromise (IoCs) or ‘known bads’.
This approach allows Darktrace to remain one step ahead of increasingly adaptive threat actors, providing organizations and their security teams with a robust AI-driven solution able to safeguard their networks in an ever-evolving threat landscape.
Credit to Charlotte Thompson, Cyber Analyst, Anna Gilbertson, Cyber Analyst.
Credential Phishing: Common attack methods and defense strategies
08
Jul 2024
Credential theft remains a top cybersecurity threat
Adversaries have many options in their arsenal to gain access into an organization.
Exploitable vulnerabilities: This can provide access into a system’s processes and allow activity within the context of the service account.
Weak or misconfigured systems: These can provide direct avenues of access into exposed systems.
However, the more desirable option is to obtain user or API credentials permitting the adversary to authenticate and operate as one of the organization’s authorized entities.
While 2023 noted a marked increase in vulnerability exploits as the chosen vector of attack, the use of credentials by adversaries still ranked #1 at 24% in the latest Verizon Data Breach Investigations Report. Mandiant’s M-Trends report noted 14% of their investigations involved stolen credentials as the attack vector, and Darktrace’s 2023 End of Year Threat Report revealed that Credential Access was one of the most observed MITRE ATT&CK tactics.
Credential phishing methods
There are many ways an adversary can obtain a user’s credentials. Some require gaining access to the target system or exploiting an application while others target the end-user directly.
Social Engineering: Many users have a habit of incorporating things in their life into their passwords. Family members, important dates, hobbies, movies, and music favorites have all been used. Adversaries know this and will scour social media to gain knowledge about their intended target. This method was beautifully demonstrated in the 1983 movie, Wargames, where Matthew Broderick’s character scours articles, papers, and video about Dr. Stephen Falken, finally guessing that the password into the WOPR (War Operations Plan Response) computer is that of his deceased child, Joshua.
Credential Cracking / Dumping: If the adversary has gained access to a targeted system, they may employ a password cracking, or credential dumping, program. For Unix-based solutions, obtaining the /etc/passwd and /etc/shadow files provides the users, groups, and encrypted passwords. Adversaries can exfiltrate these files and then utilize password crackers such as John the Ripper, Crack, or codebreaker003. Mimikatz(see more below) can also pass cache information for Mac / Unix and Linux systems.
Windows-based solutions: Adversaries have successfully utilized programs such as Mimikatz to dump credentials and hashes. Mimikatz can pass the hash string to the Local Security Authority Subsystem Service (LSASS) to authorize user actions, as well as perform “kerberoasting”. Kerberos is how Windows systems authorize users utilizing a 3-entity authentication method and symmetric key cryptography to create “tickets” that authorize requested actions. Mimikatz can use Kerberos tickets to gain non-expiring domain administration credentials (Golden Tickets) or tickets to login as a service on the network (Silver Tickets).
Post-It Notes: As organizations and applications started requiring stronger passwords that met complexity requirements, users did what you would expect to ensure they didn’t forget them. They wrote them down (this was also demonstrated in Wargames). The modern-day equivalent is to create a text file with all your passwords (or API credentials) in it – something adversaries are delighted to find.
One of the funniest, yet totally on-point, comic routines I’ve seen on this topic is Michael McIntyre’s You Should Probably Change Your Password skit at the London Palladium.
Phishing / Smishing: Forged messages requesting users to reset their passwords or directing them to enter their credentials used to be easier to spot. However, the emergence of Artificial Intelligence (AI) is allowing adversaries to create very realistic messages and web pages that mimic an organization’s authentication pages. These attempts are not just limited to email, adversaries are utilizing SMS messages and other collaborative communication solutions like Microsoft Teams to transmit fake messages to unsuspecting users. Also, security teams are seeing increased use of Quick Response (QR) codes in scam messages. QR codes are appearing in all aspects of everyday life (I’m finding it hard to go into a restaurant without having to scan a QR code to read the menu) and there is a false sense of security people have in thinking that QR codes are safe to scan.
Vulnerability Exploits: Gaining access to the credential cache or password file is not the only way adversaries can obtain user credentials. Some applications will store the user credentials in process memory (decrypted). If the application is vulnerable to a remote exploit, it can be possible for the adversary to dump the memory of the application process and locate these stored credentials. This was clearly illustrated in the Heartbleed exploit disclosed to the public in 2014.
Air Cracking: Air Cracking is specific to Wi-Fi networks and involves cracking programs that analyze wireless encrypted packets and extracting WEP or WPA/WPA2 PSK passwords (giving the adversary access to the Wi-Fi network).
Dark Web Purchase: Threat groups know how to monetize compromised credentials. Selling compromised credentials on the Dark Web occurs on a regular basis. Sites such as HaveIBeenPwned.com can assist users in determining if a particular password has been found to be compromised. Note: Users should ensure that the sites they are checking to see if their password has been compromised are actual legitimate sites and not a credential harvesting site!
What is credential stuffing and why is it so effective?
Credential Stuffing is so successful because users tend to utilize the same, or very similar, passwords across all the systems and applications they access. This includes both personal and business accounts. Once an adversary harvests credentials from one site, they will try that password on other sites, and if that fails, they can utilize generative AI to predict potential variations of the password.
How to reduce the risk of credential stuffing?
Users can help reduce exposure of their credentials by creating passwords that meet complexity requirements but are also easy to remember. A good approach is to take a phrase and apply a substitution rule. For example, let’s take the start of Charles Dicken’s book A Tale of Two Cities and create a substitution rule for it:
It was the best of times, it was the worst of times
Let’s shorten that to: Best of times Worst of times
Apply the following substitution rule: o = 0, i = 1, e = 3, spaces = @
Now my phrase becomes: B3st@0f@t1m3s@W0rst@0f@t1m3s
You now have a 28-character password that contains letters, a capital letter, number, and special character. Nobody is cracking that, and the phrase and substitution rule makes it much easier to remember (PS: 12-character passwords are also fine, taking ~34,000 years to crack using current technology).
Organizations can reduce exposure through implementation of two-factor authentication (2FA), so even if the passwords are compromised through the methods described above, another authentication layer stands in the way of the adversary.
Additionally, preventing phishing messages from landing in user’s inboxes (Email or collaborative solutions such as Microsoft Teams) is critical not only for reducing the potential exposure of user credentials, but also user’s opening malicious attachments or links. Generative AI tools such as ChatGPT have resulted in over an 135% increase in novel social engineering attacks.
How Darktrace protects against sophisticated credential phishing attempts
Malicious actors can exploit these leaked credentials to drastically lower the barrier to entry associated with brute-forcing access to their target networks. While implementing well-configured MFA and enforcing regular password changes can help protect organizations, these measures alone may not be enough to fully negate the advantage attackers gain with stolen credentials.
In early 2024, one Darktrace customer was compromised by a malicious actor after their internal credentials had been leaked on the dark web. Subsequent attack phases were detected by Darktrace/Network and the customer was alerted to the suspicious activity via the Proactive Threat Notification (PTN) service, following an investigation by Darktrace’s Security Operation Center (SOC).
Darktrace detected a device on the network of a customer in the US carrying out a string of anomalous activity indicative of network compromise. The device was observed using a new service account to authenticate to a Virtual Private Network (VPN) server, before proceeding to perform a range of suspicious activity including internal reconnaissance and lateral movement.
Unfortunately for the customer in this case, Darktrace’s autonomous response was not enabled on the network at the time of the attack. Had it been active, it would have been able to autonomously act against the malicious activity by disabling users, strategically blocking suspicious connections and limiting devices to their expected patterns of activity.
Adversaries have various methods available to compromise user and API credentials. There is no single silver bullet that will protect users and organizations, but rather, a layered approach that incorporates education, security controls such as 2FA, unsupervised AI to detect novel and sophisticated spear-phishing messages, as well as protection against exploits that give adversaries access to systems.
