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Insider Threats: Turning 2025 Intelligence into a 2026 Defense Strategy

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Insider Threats: Turning 2025 Intelligence into a 2026 Defense Strategy

In this post, we break down the 91,321 instances of insider activity observed by Flashpoint™ in 2025, examine the top five cases that defined the year, and provide the technical and behavioral red flags your team needs to monitor in 2026.

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January 15, 2026

Every organization houses sensitive assets that threat actors actively seek. Whether it is proprietary trade secrets, intellectual property, or the personally identifiable information (PII) of employees and customers, these datasets are the lifeblood of the modern enterprise—and highly lucrative commodities within the illicit underground.

In 2025, Flashpoint observed 91,321 instances of insider recruiting, advertising, and threat actor discussions involving insider-related illicit activity. This underscores a critical reality—it is far more efficient for threat actors to recruit an “insider” to circumvent multi-million dollar security stacks than it is to develop a complex exploit from the outside. 

An insider threat, any individual with authorized access, possesses the unique ability to bypass traditional security gates. Whether driven by financial gain, ideological grievances, or simple human error, insiders can potentially compromise a system with a single keystroke. To protect our customers from this internal risk, Flashpoint monitors the illicit forums and marketplaces where these threats are being solicited. 

In this post, we unpack the evolving insider threat landscape and what it means for your security strategy in 2026. By analyzing the volume of recruitment activity and the specific industries being targeted, organizations can move from a reactive posture to a proactive defense.

By the Numbers: Mapping the 2025 Insider Threat Landscape

Last year, Flashpoint collected and researched:

  • 91,321 posts of insider solicitation and service advertising
  • 10,475 channels containing insider-related illicit activity
  • 17,612 total authors

On average, 1,162 insider-related posts were published per month, with Telegram continuing to be one of the most prominent mediums for insiders and threat actors to identify and collaborate with each other. Analysts also identified instances of extortionist groups targeting employees at organizations to financially motivate them to become insiders.

Insider Threat Landscape by Industry

The telecommunications industry observed the most insider-related activity in 2025. This is due to the industry’s central role in identity verification and its status as the primary target for SIM swapping—a fraudulent technique where threat actors convince employees of a mobile carrier to link a victim’s phone number to a SIM card controlled by the attacker. This allows the threat actor to receive all the victim’s calls and texts, allowing them to bypass SMS-based two-factor authentication.

Insider Threat data from January 1, 2025 to November 24, 2025

Flashpoint analysts identified 12,783 notable posts where the level of detail or the specific target was particularly concerning.

Top Industries for Insiders Advertising Services (Supply):

  1. Telecom
  2. Financial
  3. Retail
  4. Technology

Top Industries for Threat Actors Soliciting Access (Demand):

  1. Technology
  2. Financial
  3. Telecom
  4. Retail

6 Notable Insider Threat Cases of 2025

The following cases highlight the variety of ways insiders impacted enterprise systems this year, ranging from intentional fraud to massive technical oversights.

Type of IncidentDescription
MaliciousApproximately nine employees accessed the personal information of over 94,000 individuals, making illegal purchases using changed food stamp cards.   
NonmaliciousAn unprotected database belonging to a Chinese IoT firm leaked 2.7 billion records, exposing 1.17 TB of sensitive data and plaintext passwords. 
MaliciousAn insider at a well-known cybersecurity organization was terminated after sharing screenshots of internal dashboards with the Scattered Lapsus$ Hunters threat actor group.
MaliciousAn employee working for a foreign military contractor was bribed to pass confidential information to threat actors.
MaliciousA third-party contractor for a cryptocurrency firm sold customer data to threat actors and recruited colleagues into the scheme, leading to the termination of 300 employees and the compromise of 69,000 customers.
MaliciousTwo contractors accessed and deleted sensitive documents and dozens of databases belonging to the Internal Revenue Service and US General Services Administration.

Catching the Warning Signs Early

Potential insiders often display technical and nontechnical behavior before initiating illicit activity. Although these actions may not directly implicate an employee, they can be monitored, which may lead to inquiries or additional investigations to better understand whether the employee poses an elevated risk to the organization.

Flashpoint has identified the following nontechnical warning signs associated with insiders:

  • Behavioral indicators: Observable actions that deviate from a known baseline of behaviors. These can be observed by coworkers or management or through technical indicators. Behavioral indicators can include increasingly impulsive or erratic behavior, noncompliance with rules and policies, social withdrawal, and communications with competitors.
  • Financial changes: Significant and overlapping changes in financial standing—such as significant debt, financial troubles, or sudden unexplained financial gain—could indicate a potential insider threat. In the case of financial distress, an employee can sell their services to other threat actors via forums or chat services, thus creating additional funding streams while seeming benign within their organization.
  • Abnormal access behavior: Resistance to oversight, unjustified requests for sensitive information beyond the employee’s role, or the employee being overprotective of their access privileges might indicate malicious intent.
  • Separation on bad terms: Employees who leave an organization under unfavorable circumstances pose an increased insider threat risk, as they might want to seek revenge by exploiting whatever access they had or might still possess after leaving.
  • Odd working hours: Actors may leverage atypical after-hours work to pursue insider threat activity, as there is less monitoring. By sticking to an atypical schedule, threat actors maintain a cover of standard work activity while pursuing illicit activity simultaneously.
  • Unusual overseas travel: Unusual and undocumented overseas travel may indicate an employee’s potential recruitment by a foreign state or state-sponsored actor. Travel might be initiated to establish contact and pass sensitive information while avoiding raising suspicions in the recruit’s home country.

The following are technical warning signs:

  • Unauthorized devices: Employees using unauthorized devices for work pose an insider threat, whether they have malicious intent or are simply putting themselves at higher risk of human error. Devices that are not controlled and monitored by the organization fall outside of its scope of operational security, while still carrying all of the sensitive data and configuration of the organization.
  • Abnormal network traffic: An unusual increase in network traffic or unexplained traffic patterns associated with the employee’s device that differ from their normal network activity could indicate malicious intent. This includes network traffic employing unusual protocols, using uncommon ports, or an overall increase in after-hours network activity.
  • Irregular access pattern: Employees accessing data outside the scope of their job function may be testing and mapping the limits of their access privileges to restricted areas of information as they evaluate their exfiltration capabilities for their planned illicit actions.
  • Irregular or mass data download: Unexpected changes in an employee’s data handling practices, such as irregular large-scale downloads, unusual data encryption, or uncharacteristic or unauthorized data destinations, are significant indicators of an insider threat.

Insider Threats: What to Expect in 2026

As 2026 unfolds, insider threat actors will continue to be a major threat to organizations. Ransomware groups and initial access threat actors will continue recruiting interested insiders and exploiting human vulnerabilities through social engineering tactics. Following Telegram’s recent bans on many illicit groups and channels, Flashpoint assesses that threat actors are likely to migrate to different platforms, such as Signal, where encrypted chats make their activity harder to monitor.

As AI technologies continue to advance, organizations will be better equipped to identify and mitigate insider risks. At the same time, threat actors will likely increasingly abuse AI and other tools to access sensitive information. 
Is your organization equipped to spot the warning signs? Request a demo to learn more and to mitigate potential risk from within your organization.

Request a demo today.

The post Insider Threats: Turning 2025 Intelligence into a 2026 Defense Strategy appeared first on Flashpoint.

Closing the Door on Net-NTLMv1: Releasing Rainbow Tables to Accelerate Protocol Deprecation

15 January 2026 at 15:00

Written by: Nic Losby


Introduction

Mandiant is publicly releasing a comprehensive dataset of Net-NTLMv1 rainbow tables to underscore the urgency of migrating away from this outdated protocol. Despite Net-NTLMv1 being deprecated and known to be insecure for over two decades—with cryptanalysis dating back to 1999—Mandiant consultants continue to identify its use in active environments. This legacy protocol leaves organizations vulnerable to trivial credential theft, yet it remains prevalent due to inertia and a lack of demonstrated immediate risk.

By releasing these tables, Mandiant aims to lower the barrier for security professionals to demonstrate the insecurity of Net-NTLMv1. While tools to exploit this protocol have existed for years, they often required uploading sensitive data to third-party services or expensive hardware to brute-force keys. The release of this dataset allows defenders and researchers to recover keys in under 12 hours using consumer hardware costing less than $600 USD. This initiative highlights the amplified impact of combining Mandiant's frontline expertise with Google Cloud's resources to eliminate entire classes of attacks.

This post details the generation of the tables, provides access to the dataset for community use, and outlines critical remediation steps to disable Net-NTLMv1 and prevent authentication coercion attacks.

Background

Net-NTLMv1 has been widely known to be insecure since at least 2012, following presentations at DEFCON 20, with cryptanalysis of the underlying protocol dating back to at least 1999. On Aug. 30, 2016, Hashcat added support for cracking Data Encryption Standard (DES) keys using known plaintext, further democratizing the ability to attack this protocol. Rainbow tables are almost as old, with the initial paper on rainbow tables published in 2003 by Philippe Oechslin, citing an earlier iteration of a time-memory trade-off from 1980 by Martin Hellman.

Essentially, if an attacker can obtain a Net-NTLMv1 hash without Extended Session Security (ESS) for the known plaintext of 1122334455667788, a cryptographic attack, referred to as a known plaintext attack (KPA), can be applied. This guarantees recovery of the key material used. Since the key material is the password hash of the authenticating Active Directory (AD) object—user or computer—the attack results can quickly be used to compromise the object, often leading to privilege escalation.

A common chain attackers use is authentication coercion from a highly privileged object, such as a domain controller (DC). Recovering the password hash of the DC machine account allows for DCSync privileges to compromise any other account in AD.

Dataset Release

The unsorted dataset can be downloaded using gsutil -m cp -r gs://net-ntlmv1-tables/tables . or through the Google Cloud Research Dataset portal

The SHA512 hashes of the tables can be checked by first downloading the checksums gsutil -m cp gs://net-ntlmv1-tables/tables.sha512 . then checked by sha512sum -c tables.sha512. The password cracking community has already created derivative work and is also hosting the ready to use tables.

Use of the Tables

Once a Net-NTLMv1 hash has been obtained, the tables can be used with historical or modern reinventions of rainbow table searching software such as rainbowcrack (rcrack), or RainbowCrack-NG on central processing units (CPUs) or a fork of rainbowcrackalack on graphics processing units (GPUs). The Net-NTLMv1 hash needs to be preprocessed to the DES components using ntlmv1-multi as shown in the next section.

Obtaining a Net-NTLMv1 Hash

Most attackers will use Responder with the --lm and --disable-ess flags and set the authentication to a static value of 1122334455667788 to only allow for connections with Net-NTLMv1 as a possibility. Attackers can then wait for incoming connections or coerce authentication using a tool such as PetitPotam or DFSCoerce to generate incoming connections from DCs or lower privilege hosts that are useful for objective completion. Responses can be cracked to retrieve password hashes of either users or computer machine accounts. A sample workflow for an attacker is shown below in Figure 1, Figure 2, and Figure 3.

DFSCoerce against a DC

Figure 1: DFSCoerce against a DC

Net-NTLMv1 hash obtained for DC machine account

Figure 2: Net-NTLMv1 hash obtained for DC machine account

Parse Net-NTLMv1 hash to DES parts

Figure 3: Parse Net-NTLMv1 hash to DES parts

Figure 4 illustrates the processing of the Net-NTLMv1 hash to the DES ciphertexts.

Net-NTLMv1 hash to DES ciphertexts

Figure 4: Net-NTLMv1 hash to DES ciphertexts

An attacker then takes the split-out ciphertexts to crack the keys used based on the known plaintext of 1122334455667788 with the steps of loading the tables shown in Figure 5 and cracking results in Figure 6 and Figure 7.

Loading DES components for cracking

Figure 5: Loading DES components for cracking

First hash cracked

Figure 6: First hash cracked

Second hash cracked and run statistics

Figure 7: Second hash cracked and run statistics

An attacker can then calculate the last remaining key with ntlmv1-multi once again, or look it up with twobytes, to recreate the full NT hash for the DC account with the last key part shown in Figure 8.

Calculate remaining key

Figure 8: Calculate remaining key

The result can be checked with hashcat's NT hash shucking mode, -m 27000, as shown in Figure 9.

Keys checked with hash shucking

Figure 9: Keys checked with hash shucking

An attacker can then use the hash to perform a DCSync attack targeting a DC and authenticating as the now compromised machine account. The attack flow uses secretsdump.py from the Impacket toolsuite and is shown in Figure 10.

DCSync attack performed

Figure 10: DCSync attack performed

Remediation

Organizations should immediately disable the use of Net-NTLMv1. 

Local Computer Policy

"Local Security Settings" > "Local Policies" > "Security Options" > “Network security: LAN Manager authentication level" > "Send NTLMv2 response only".

Group Policy

"Computer Configuration" > "Policies" > "Windows Settings" > "Security Settings" > "Local Policies" > "Security Options" > "Network Security: LAN Manager authentication level" > "Send NTLMv2 response only"

As these are local to the computer configurations, attackers can and have set the configuration to a vulnerable state to then fix the configuration after their attacks have completed with local administrative access. Monitoring and alerting of when and where Net-NTLMv1 is used is needed in addition to catching these edge cases.

Filter Event Logs for Event ID 4624: "An Account was successfully logged on." > "Detailed Authentication Information" > "Authentication Package" > "Package Name (NTLM only)", if "LM" or "NTLMv1" is the value of this attribute, LAN Manager or Net-NTLMv1 was used.

Related Reading

This project was inspired by and referenced the following research published to blogs, social media, and code repositories.

Acknowledgements

Thank you to everyone who helped make this blog post possible, including but not limited to Chris King and Max Gruenberg.

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