T1557: Adversary-in-the-Middle
Adversaries may attempt to position themselves between two or more networked devices using an adversary-in-the-middle (AiTM) technique to support follow-on behaviors such as Network Sniffing, Transmitted Data Manipulation, or replay attacks (Exploitation for Credential Access). By abusing features of common networking protocols that can determine the flow of network traffic (e.g. ARP, DNS, LLMNR, etc.), adversaries may force a device to communicate through an adversary controlled system so they can collect information or perform additional actions.[1]
For example, adversaries may manipulate victim DNS settings to enable other malicious activities such as preventing/redirecting users from accessing legitimate sites and/or pushing additional malware.[2][3][4] Adversaries may also manipulate DNS and leverage their position in order to intercept user credentials, including access tokens (Steal Application Access Token) and session cookies (Steal Web Session Cookie).[5][6] Downgrade Attacks can also be used to establish an AiTM position, such as by negotiating a less secure, deprecated, or weaker version of communication protocol (SSL/TLS) or encryption algorithm.[7][8][9]
Adversaries may also leverage the AiTM position to attempt to monitor and/or modify traffic, such as in Transmitted Data Manipulation. Adversaries can setup a position similar to AiTM to prevent traffic from flowing to the appropriate destination, potentially to impair defenses and/or in support of a Network Denial of Service.
Analyst context for executives and security teams
Adversary-in-the-Middle matters because it can turn ordinary network connectivity into a credential, token, session, or traffic-manipulation exposure. If an attacker can influence DNS, ARP, DHCP, name resolution, Wi-Fi, TLS negotiation, or similar traffic paths, they may collect information, redirect users, intercept credentials or access tokens, modify transmitted data, or disrupt traffic flows. For leaders, this is not only a network issue: it affects identity assurance, cloud/session security, SOC visibility, and the reliability of critical communications.
Executive priority
Prioritize this technique where business operations depend on trusted network paths, remote access, cloud identity sessions, Wi-Fi access, DNS integrity, or network devices. Executives should ask whether the organization can prove that sensitive traffic is encrypted, name resolution is controlled, legacy or weak protocols are reduced, and network changes are monitored. This is also relevant to audit and resilience discussions because AiTM can undermine authentication, data integrity, and incident scoping if telemetry is incomplete.
Technical view
ATT&CK lists this technique under credential access and collection across Linux, macOS, Windows, and network devices. Defensive validation should focus on whether teams can detect network and configuration anomalies associated with the related sub-techniques: Name Resolution Poisoning and SMB Relay, ARP Cache Poisoning, DHCP Spoofing, and Evil Twin. Since ATT&CK provides no official detection text for T1557, use the related detection strategy DET0296 as direction: validate visibility into network path changes, suspicious name resolution responses, unexpected DHCP behavior, ARP anomalies, Wi-Fi impersonation indicators, DNS manipulation, and TLS downgrade or weak-protocol negotiation where observable. IR teams should treat suspected AiTM as both a network investigation and an identity/session exposure assessment, especially where credentials, access tokens, or session cookies may have traversed the affected path.
Likely telemetry
- DNS query/response logs, resolver configuration changes, and unexpected DNS redirection evidence
- ARP table changes, duplicate IP/MAC observations, and local network switching telemetry where available
- DHCP server, lease, option, and rogue DHCP indicators
- LLMNR, NBT-NS, and mDNS traffic observations, especially on Windows segments
- Wireless access point inventory, SSID/BSSID observations, and client association logs for Evil Twin scenarios
Detection direction
- Map coverage by sub-technique rather than treating AiTM as one alert: Windows name-resolution abuse, ARP poisoning, DHCP spoofing, and Evil Twin require different sensors and baselines.
- Validate that DET0296-style network and configuration anomaly detection is supported by actual telemetry, not assumed from endpoint logs alone.
- Tune for environment-specific false positives such as legitimate network reconfiguration, DHCP failover, DNS migrations, proxy changes, wireless testing, or vulnerability assessments.
- Correlate network anomalies with credential-access and collection outcomes, including suspicious authentication events after traffic redirection or token/session exposure concerns.
- Review blind spots on unmanaged networks, guest Wi-Fi, remote sites, network devices, and segments where packet/flow visibility is limited.
Mitigation priorities
- Start with encryption of sensitive information in transit and reduction of weak or deprecated protocol negotiation where feasible, aligning with M1041.
- Reduce exploitable network paths through segmentation, traffic filtering, and restricted access to network resources, aligning with M1030, M1037, and M1035.
- Use network intrusion prevention or detection signatures at appropriate boundaries where traffic patterns are observable, aligning with M1031.
- Disable or remove unnecessary legacy features, services, or protocols that increase exposure to name-resolution, downgrade, or traffic-redirection abuse, aligning with M1042.
- Include user training for reporting suspicious Wi-Fi, certificate warnings, unexpected redirects, or authentication prompts, aligning with M1017, but do not rely on training as the primary control.
Analyst notes and limits
ATT&CK relationships show use by several groups, software, and a campaign, and identify specific sub-techniques that make this behavior operationally meaningful. The most useful defensive framing is coverage validation: can the organization see and investigate manipulation of traffic direction and name resolution before it becomes credential theft, token theft, data manipulation, or disruption? Network device and identity teams should be included in tabletop and detection-engineering work because AiTM can cross traditional SOC ownership boundaries.
The supplied ATT&CK object does not include official detection text, and the relationship descriptions are not sufficient to claim local exposure, active exploitation, or guaranteed detection. This take is based only on the provided ATT&CK fields, external references, and relationships. Actual priority depends on local network architecture, wireless use, identity provider/session controls, encryption posture, sensor placement, and retained logs.
Adversary-in-the-Middle
Adversaries may attempt to position themselves between two or more networked devices using an adversary-in-the-middle (AiTM) technique to support follow-on behaviors such as Network Sniffing, Transmitted Data Manipulation, or replay attacks (Exploitation for Credential Access). By abusing features of common networking protocols that can determine the flow of network traffic (e.g. ARP, DNS, LLMNR, etc.), adversaries may force a device to communicate through an adversary controlled system so they can collect information or perform additional actions.[1]
For example, adversaries may manipulate victim DNS settings to enable other malicious activities such as preventing/redirecting users from accessing legitimate sites and/or pushing additional malware.[2][3][4] Adversaries may also manipulate DNS and leverage their position in order to intercept user credentials, including access tokens (Steal Application Access Token) and session cookies (Steal Web Session Cookie).[5][6] Downgrade Attacks can also be used to establish an AiTM position, such as by negotiating a less secure, deprecated, or weaker version of communication protocol (SSL/TLS) or encryption algorithm.[7][8][9]
Adversaries may also leverage the AiTM position to attempt to monitor and/or modify traffic, such as in Transmitted Data Manipulation. Adversaries can setup a position similar to AiTM to prevent traffic from flowing to the appropriate destination, potentially to impair defenses and/or in support of a Network Denial of Service.
How security teams should use this page
Treat this object as behavior context, not an attribution claim. Validate the related groups, software, data sources, and mitigations against official ATT&CK relationships and your own telemetry before making control-coverage decisions.
Related techniques
This mirrors the MITRE pattern of making group, software, campaign, and technique relationships scannable. Relationship notes come from mirrored ATT&CK relationship text when available.
| Domain | ID | Name | Relationship / procedure |
|---|---|---|---|
| Enterprise | T1557.003 | DHCP Spoofing Sub-technique | DHCP Spoofing subtechnique of this object. |
| Enterprise | T1557.002 | ARP Cache Poisoning Sub-technique | ARP Cache Poisoning subtechnique of this object. |
| Enterprise | T1557.001 | Name Resolution Poisoning and SMB Relay Sub-technique | Name Resolution Poisoning and SMB Relay subtechnique of this object. |
| Enterprise | T1557.004 | Evil Twin Sub-technique | Evil Twin subtechnique of this object. |
Groups, software, and campaigns
G0129: Mustang Panda
Mustang Panda is a China-based cyber espionage threat actor that has been conducting operations since at least 2012. Mustang Panda has been known to use tailored phishing lures and decoy documents to deliver malicious payloads. Mustang Panda has targeted government, diplomatic, and non-governmental organizations, including think tanks, religious institutions, and research entities, across the United States, Europe, and Asia, with notable activity in Russia, Mongolia, Myanmar, Pakistan, and Vietnam. [1][2][3][4][5][6][7][8][9][10][11][12][13]
G0094: Kimsuky
Kimsuky is a Democratic People's Republic of Korea (DPRK)-based cyber espionage group that has been active since at least 2012. The group initially targeted South Korean government agencies, think tanks, and subject-matter experts in various fields. Its operations expanded to include the United Nations and organizations in the government, education, business services, and manufacturing sectors across the United States, Japan, Russia, and Europe. Kimsuky has focused collection on foreign policy and national security issues tied to the Korean Peninsula, nuclear policy, and sanctions. Kimsuky operations have overlapped with those of other North Korean state-sponsored cyber espionage actors as a result of ad hoc collaborations or other limited resource sharing.[1][2][3][4][5][6]
Kimsuky was assessed to be responsible for the 2014 Korea Hydro & Nuclear Power Co. compromise; other notable campaigns include Operation STOLEN PENCIL (2018), Operation Kabar Cobra (2019), and Operation Smoke Screen (2019).[7][8][9] In 2023, Kimsuky was observed using commercial large language models (LLMs) to assist with vulnerability research, scripting, social engineering and reconnaissance.[10]
DPRK threat actor cluster boundaries overlap in open source reporting, with some security researchers consolidating all attributed North Korean state-sponsored cyber activity under Lazarus Group, rather than tracking operationally distinct subgroups.
G1041: Sea Turtle
Sea Turtle is a Türkiye-linked threat actor active since at least 2017 performing espionage and service provider compromise operations against victims in Asia, Europe, and North America. Sea Turtle is notable for targeting registrars managing ccTLDs and complex DNS-based intrusions where the threat actor compromised DNS providers to hijack DNS resolution for ultimate victims, enabling Sea Turtle to spoof log in portals and other applications for credential collection.[1][2][3][4]
S0281: Dok
Dok is a Trojan application disguised as a .zip file that is able to collect user credentials and install a malicious proxy server to redirect a user's network traffic (i.e. Adversary-in-the-Middle).[1][2][3]
S9003: evilginx2
S1131: NPPSPY
NPPSPY is an implementation of a theoretical mechanism first presented in 2004 for capturing credentials submitted to a Windows system via a rogue Network Provider API item. NPPSPY captures credentials following submission and writes them to a file on the victim system for follow-on exfiltration.[1][2]
S1188: Line Runner
Line Runner is a persistent backdoor and web shell allowing threat actors to upload and execute arbitrary Lua scripts. Line Runner is associated with the ArcaneDoor campaign.[1][2]
C0046: ArcaneDoor
ArcaneDoor is a campaign targeting networking devices from Cisco and other vendors between July 2023 and April 2024, primarily focused on government and critical infrastructure networks. ArcaneDoor is associated with the deployment of the custom backdoors Line Runner and Line Dancer. ArcaneDoor is attributed to a group referred to as UAT4356 or STORM-1849, and is assessed to be a state-sponsored campaign.[1][2]
All related ATT&CK context
Mitigation direction
Object version and sync metadata
The fields below describe the current mirrored snapshot. When Glexia retains multiple ATT&CK source imports, you can open the table to compare the same object across releases (hashes and MITRE timestamps). For MITRE’s own release notes and roadmap, see ATT&CK resources — Updates .
Imported snapshots across ATT&CK releases (1)
| Release | Bundle imported | Object version | Modified | Status | Raw hash |
|---|---|---|---|---|---|
| 19.1 | 2.5 | Current bundle | 0dd83b905042… |
Mirrored ATT&CK source object
The raw object is retained through the mirrored ATT&CK source bundle and object hash. The raw endpoint returns the exact object from the mirrored bundle when available.
External references and citations
MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.
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[1]
Rapid7 MiTM Basics
Rapid7. (n.d.). Man-in-the-Middle (MITM) Attacks. Retrieved March 2, 2020.
Open source URL -
[2]
ttint_rat
Tu, L. Ma, Y. Ye, G. (2020, October 1). Ttint: An IoT Remote Access Trojan spread through 2 0-day vulnerabilities. Retrieved October 28, 2021.
Open source URL -
[3]
dns_changer_trojans
Abendan, O. (2012, June 14). How DNS Changer Trojans Direct Users to Threats. Retrieved October 28, 2021.
Open source URL -
[4]
ad_blocker_with_miner
Kuzmenko, A.. (2021, March 10). Ad blocker with miner included. Retrieved October 28, 2021.
Open source URL -
[5]
volexity_0day_sophos_FW
Adair, S., Lancaster, T., Volexity Threat Research. (2022, June 15). DriftingCloud: Zero-Day Sophos Firewall Exploitation and an Insidious Breach. Retrieved July 1, 2022.
Open source URL -
[6]
Token tactics
Microsoft Incident Response. (2022, November 16). Token tactics: How to prevent, detect, and respond to cloud token theft. Retrieved December 26, 2023.
Open source URL -
[7]
mitm_tls_downgrade_att
praetorian Editorial Team. (2014, August 19). Man-in-the-Middle TLS Protocol Downgrade Attack. Retrieved December 8, 2021.
Open source URL -
[8]
taxonomy_downgrade_att_tls
Alashwali, E. S., Rasmussen, K. (2019, January 26). What's in a Downgrade? A Taxonomy of Downgrade Attacks in the TLS Protocol and Application Protocols Using TLS. Retrieved December 7, 2021.
Open source URL -
[9]
tlseminar_downgrade_att
Team Cinnamon. (2017, February 3). Downgrade Attacks. Retrieved December 9, 2021.
Open source URL -
[10]
mitre-attack T1557Open source URL
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