Report | AI Security Report, 2025
AI is powering a new era of cyber threats. The AI Security Report 2025 by Check Point Research exposes how attackers are weaponizing AI for phishing, deepfakes, malware, and more. Discover how to fight back with AI-driven defense. Download the report now to stay ahead of the threats.
CHECK POINT RESEARCH
AI SECURITY REPORT
C H E C K P O I N T A I R E P O R T • 1 S T A N N U A L E D I T I O N
TABLE OF CONTENTS
01 INTRODUCTION
02 AI THREATS
04 AI FOR ENTERPRISES
03 AI FOR RESEARCH
05 SECURITY FOR, BY & WITH AI
01 INTRODUCTION01
INTRODUCTION The Accelerating Future of AI for Cyber Offenders and Defenders
AI is revolutionizing industries, and cyber crime and cyber security are no different. Adopting AI in enterprises—and unfortunately by threat actors as well—enhances efficiency, scale, and impact. At this point in time, we believe it’s essential to pause and assess the current state and future of AI and cyber security.
How are attackers using AI, and what comes next? As cyber defenders, how can we leverage AI to enhance our security efforts and protect our organizations more effectively? These are the questions addressed in the first edition of the Check Point Research AI Security Report. Our focus zeroes in on:
• The rise of autonomous and interactive social engineering across text, audio, and video
• The jailbreaking and weaponization of LLMs
• The automation of malware development and data mining
• AI adoption in enterprises and their associated risks
• The emergence of data poisoning in the wild and large- scale disinformation amplified by GenAI tools
• The AI tools that fight fire with fire- protecting your organization from the most advanced threats
AI threats are no longer theoretical—they're here and evolving rapidly. As access to AI tools becomes more widespread, threat actors exploit this shift in two key ways: by leveraging AI to enhance their capabilities and targeting organizations and individuals adopting AI technologies.
The following pages provide a comprehensive understanding of these threats, allowing readers to navigate the intricate landscape of AI security.
To a secure future of innovation and success,
Lotem Finkelstein, Director of Check Point Research
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THE AI MODELS USED BY CYBER CRIMINALS Cyber criminals are closely monitoring trends in mainstream AI adoption. Whenever a new large language model (LLM) is released to the public, underground actors quickly test its potential for
misuse (figure 1). Currently, ChatGPT and OpenAI’s API are the most popular models for cyber criminals, while others like Google Gemini, Microsoft Copilot, and Anthropic Claude are quickly gaining popularity. The landscape is changing with the launch of open-source models like DeepSeek and Qwen by Alibaba. These models enhance accessibility, have minimal usage restrictions, and are available in free tiers, making them a key asset to crime.
Figure 1 – Underground forum discussion on harnessing DeepSeek for malware development
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The Development of Malicious AI Models
Cyber criminals are exploiting mainstream platforms and creating and selling specialized malicious LLM models explicitly tailored for cyber crime (figure 2). These dark LLM models are designed to circumvent the safeguards established for ethical models and are actively marketed as hacking tools.
Figure 2 – Onion GPT, an example of a dark AI model created in Tor
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Figure 3 – Malicious AI service WormGPT advertised on a Telegram channel
The notorious AI model WormGPT was created by jailbreaking ChatGPT (figure 3). Marketed as the “ultimate hacking AI,” it can generate phishing emails, write malware, and craft social engineering scripts without ethical constraints. A Telegram channel promotes its use for fraud, botnet creation, and cyber intrusion, offering subscriptions highlighting the commercialization of dark AI.
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Figure 4 - HackerGPT capabilities published on a cyber crime forum
A new wave of dark AI models, such as GhostGPT, FraudGPT, and HackerGPT (figure 4), serve specific aspects of cyber crime. Some models wrap around mainstream AI with jailbreaks,
while others modify open-source models. As mainstream AI models evolve, so do their dark counterparts.
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The Rise of Fake AI Platforms
The demand for AI-based services has led to the emergence of fake AI platforms designed to deceive users and distribute malware, steal sensitive data, or enable financial fraud. Examples include HackerGPT Lite (figure 5), which seems to be an AI tool but is suspected to be a phishing website and fake DeepSeek download sites, which, in reality, distribute malware.
In one case, a malicious distributed Chrome extension imitating ChatGPT was designed to steal user credentials. Once installed, this harmful extension hijacked Facebook session cookies, granting attackers complete access to victims' accounts and allowing them to operate remotely.
Figure 5 - A suspected phishing AI-service website targeting cyber criminals
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https://www.ctinc.com/fake-chatgpt-chrome-extension-poses-serious-threat-to-online-security/
Social engineering—manipulating individuals into actions they wouldn’t typically take—is central to many cyber attacks. Even attacks that exploit purely technical vulnerabilities often begin with a social engineering scheme. Attackers leverage text, audio, and imagery to convincingly impersonate specific individuals or generate human voices, fostering trust to deceive their targets. With recent advancements in AI, attackers can create authentic-looking materials at scale, conduct automated chats, and hold real-time audio and video conferences while impersonating others.
As these AI-driven tools proliferate on criminal forums and incidents rise, our reliance on audio and visual cues for identity confirmation is critically compromised. Fully autonomous audio deepfake tools for large-scale phone scams are already available, meaning that recognizing a familiar face or voice is no longer sufficient proof of identity; instead, interactions must be reinforced by additional authentication measures.
AI-driven social engineering is already influencing real-world cyber crime. The FBI recently warned the public that cyber criminals increasingly leverage AI-generated text, images, audio, and video to enhance their attacks. This demonstrates that attackers
now possess sophisticated capabilities previously unavailable, significantly boosting the effectiveness of deception and fraud.
Online fraud relies on both quality and quantity. Even poorly phrased scams, like sextortion emails—can be profitable when sent to millions of potential victims, even if they succeed with only a small percentage. Attackers, therefore, aim to enhance both the quality of their impersonations and the automation level of their operations, maximizing impact while minimizing costly human resources. AI facilitates advancements in these areas, creating highly convincing text, audio, and video in multiple languages and enabling interactive chatbots, resulting in automated agents capable of engaging maliciously with victims.
A Deep Dive into Deepfake Technologies and Their Exploitation The following sections will examine the exploitation scenarios involving each media type (text, audio, images, and video), detail the related services actively advertised within criminal forums, and analyze reports from actual incidents to illustrate the practical implications of these evolving threats.
AI-POWERED SOCIAL ENGINEERING: A NEW ERA
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https://www.ic3.gov/PSA/2024/PSA241203
MEDIA TYPE OFFLINE GENERATION REALTIME GENERATION FULLY AUTONOMOUS
TEXT Pre-renderedscripts or emails
Real-time generated responses
AI-generated, fully interactive conversations
AUDIO Pre-recorded impersonations Real-time voice manipulation
Fully AI-driven conversational audio
VIDEO Pre-created deepfake videos Live face-swapping or video alteration
Completely automated, AI-generated interactive video
(Red V marks maturity level already available in markets and exploited in the wild)
The Maturity Levels of Deepfake Automation
Generative AI technologies span a spectrum of sophistication and maturity, ranging from basic offline generation of text, images, and videos to advanced online manipulation requiring real individuals’ involvement, such as face-swapping or voice imitation. At the highest level, fully autonomous, real-time generation produces convincing content instantly, dynamically responding to unsuspecting individuals during interactions.
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AI-Generated Textual Social Engineering
The availability of ChatGPT and other Large Language Model (LLM) chatbots since 2022 has lowered barriers to generating convincing text, enhancing the quality of phishing emails. Attackers, who often come from different linguistic and cultural backgrounds from their victims, previously faced significant language barriers. However, LLM technology now allows attackers to craft messages effortlessly with native-like fluency and cultural nuances.
In a recent case, Check Point Harmony Email & Collaboration blocked a sextortion campaign that used diverse textual phrasing to avoid detection. Each email in the thousands of messages uniquely reworded the urgency of "Time is running out," using expressions like "The hourglass is nearly empty for you" or "You're approaching the end of your time." Since sextortion campaigns typically do not contain traditional Indications of Compromise (IoCs) like malicious URLs or attachments, apart from cryptocurrency wallet addresses, detection relies heavily on text analysis, further complicating defense measures.
LLM technology now allows attackers to
craft messages effortlessly with
native-like fluency and cultural
nuances.
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A review of Darkweb forums reveals various AI-assisted tools designed specifically to streamline the creation and management of phishing and spam campaigns (figure 6). For example, GoMailPro (figure 8), priced at $500 per month, integrated ChatGPT in 2023 to automate the generation of spam and phishing emails. Recent 2024 updates, including capabilities to recover blocked email accounts used for spam distribution, further enhanced the solution.
Figure 6 – Advertisement of an AI-assisted Spam Agent
Figure 7 – Advertisement of an AI-assisted Spam Agent
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Another example of an AI textual application is the "Business Invoice Swapper” (figure 8) developed by the cyber criminal group GXC Team. It is designed to facilitate Business Email Compromise (BEC) by automatically scanning compromised email accounts for invoices or payment instructions. It alters banking details to redirect funds to attacker-controlled accounts. Leveraging AI, it seamlessly overcomes language barriers, manages large data volumes efficiently, and automates distribution, enhancing the scalability and impact of fraudulent email attacks.
Figure 8 – Advertisement of the“Business Invoice Swapper” in a dark web forum
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The X137 Telegram management console, advertised on Darkweb forums, is an example of a fully autonomous AI-based textual interactive agent. This tool automates tasks within text-based platforms, simultaneously conducting real-time conversations with multiple users according to designated tasks. Using Gemini AI, X137 monitors, summarizes, and engages in Telegram communications with uncensored, hacking-related insights.
The primary contribution of these AI-driven tools is their ability to scale criminal operations, overcoming previous bottlenecks associated with employing linguistically and culturally proficient manpower. AI-generated text enables cyber criminals to overcome language and cultural barriers, significantly enhancing their ability to execute sophisticated real-time and offline communication attacks. Uncensored LLM-based chatbots can convincingly and effectively manage multiple communication threads simultaneously.
In addition to financially motivated criminals, nation-state actors are also increasingly leveraging generative AI to enhance social engineering schemes. Google reports that Iranian, Russian, and Chinese APT and information operations actors have used AI tools like Gemini for content creation, localization, and persona development. OpenAI’s report similarly found the use of these capabilities in influence operations, streamlining phishing, influence campaigns, and reconnaissance.
AI-Generated Audio Deep Fakes
Cyber criminals increasingly employ AI-generated audio, or "audio deepfakes," to execute sophisticated impersonation scams. This technology produces highly realistic replicas of individuals' voices, enhancing scammers' ability to deceive victims. Voice samples on social media—from celebrities to everyday users—provide ample resources for attackers.
Popular platforms like ElevenLabs and tools like the open- source Retrieval-based Voice Conversion (RVC) algorithm can produce convincing audio using just ten minutes of voice samples. These technologies have been used in extortion cases where criminals falsely claim that a family member has been kidnapped or in an emergency and ask for urgent money transfers.
A recent case in Italy reportedly involved scammers employing live AI-assisted audio deepfake technology to convincingly impersonate the voice of defense minister Guido Crosetto. The attackers aimed to extort money from his affluent contacts by falsely claiming the funds were needed for hostage release. Several high-profile individuals, including designer Giorgio Armani, were targeted. At least one victim who knew the minister was deceived and transferred a significant sum.
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https://cloud.google.com/blog/topics/threat-intelligence/adversarial-misuse-generative-ai https://downloads.ctfassets.net/kftzwdyauwt9/5IMxzTmUclSOAcWUXbkVrK/3cfab518e6b10789ab8843bcca18b633/Threat_Intel_Report.pdf https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI https://www.businessinsider.com/ai-scam-voice-clone-fake-kidnap-call-mother-money-ransom-2023-4 https://nypost.com/2024/10/18/us-news/scammers-swindle-elderly-california-man-out-of-25k-by-using-ai-voice-technology-to-claim-his-son-was-in-horrible-accident-needed-money-for-bail-absolutely-his-voice https://www.theguardian.com/world/2025/feb/10/ai-phone-scam-targets-italian-business-leaders-including-giorgio-armani
Recent research shows that people can no longer reliably distinguish between genuine voices and AI-generated audio. Discussions on criminal forums increasingly focus on integrating AI-generated text and audio into comprehensive
criminal telephony systems (figure 9). Advertisements on dark web forums explicitly seek AI developers to implement AI-driven capabilities into phone-based scams.
Figure 9 - Recruitment of AI developers for telephony system
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https://www.researchgate.net/publication/384698535_People_are_poorly_equipped_to_detect_AI-powered_voice_clones
Figure 10 - Ad presenting the capabilities of an AI-enhanced telephony system
These AI-based call systems are already available for purchase and are primarily used as OTP bots. The bots call potential victims and follow predetermined scripts to obtain one-time password, mostly to financial services accounts.. More advanced platforms now provide flexible conversational structures, adapting scenarios in real time by analyzing victim responses dynamically.
One service launched in January 2025 highlights how these systems can seamlessly manage numerous languages and handle multiple simultaneous interactions, significantly enhancing scalability compared to traditional phone scams that rely extensively on skilled human labor (figure 10). In a conversation with such a seller, they explained, “We make a skeleton of a speech, according to which the AI will guide the client,” emphasizing its capability to go off-script and manage unexpected scenarios across "any topic, any field, any language, any voice.”
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https://securelist.com/2fa-phishing/112805/ https://www.theguardian.com/money/2025/mar/05/deepfakes-cash-and-crypto-how-call-centre-scammers-duped-6000-people
Figure 11 - Conversation with telephony system seller
Sample conversations were provided in Russian and Spanish, showcasing the system's multilingual proficiency (figure 11). These advanced AI telephony platforms cost about $20,000, including training and support, or are billed at approximately $500 base rate plus $1.50 per minute, dramatically reducing the need for many qualified human operators.
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AI-Generated Visual Deep Fakes
Criminal forums show the growing use of AI-generated images to bypass Know Your Customer (KYC) identity verification. Basic AI-driven services offer the ability to create convincing identities to register new accounts fraudulently, unlock frozen accounts, or hijack legitimate accounts by forging user identities. Prices typically start around $70 for simple AI-generated images. More sophisticated criminal services targeting major KYC providers - such as ONFIDO, SUMSUB, and JUMIO - command higher fees or even demand a percentage of the funds from hijacked accounts (figure 12).
Figure 12 - Account verification and unlocking service advertisement
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Figure 13 - Cyber crime service that offers KYC verification.
Criminal service providers typically receive the verification link and directly complete the identity verification (figure 13). Pricing varies by region, with European and CIS countries paying around $350 and services for the US and Canada reaching up to $500, especially for falsified documents. Trusting anonymous criminals with access to frozen accounts carries significant risk for clients; such transactions are feasible only due to established reputation mechanisms and comprehensive mitigation procedures within these illicit marketplaces.
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Pre-recorded Deepfake Videos
AI-generated video deepfakes are increasingly exploited for fraud, primarily through face and audio swapping in pre- recorded videos. These videos often falsely depict well-known individuals endorsing scams, including investment fraud. The technical barrier is lower for pre-recorded deepfake videos, making these services widely accessible in criminal forums. Prices range from a few hundred to several thousand dollars, depending on video length and quality. A recent operation in Tbilisi, Georgia, used deepfake videos featuring public figures such as Ben Fogle and Martin Lewis to promote fraudulent cryptocurrency investments, deceiving over 6,000 victims in the UK and Canada and resulting in $35 million in losses. Beyond financial fraud, AI-fabricated videos have been widely deployed in political influence campaigns and election-related disinformation efforts worldwide.
Real-time Video Manipulation
While pre-recorded deepfake videos are common, real-time video manipulation presents a more advanced challenge. Though high-end AI video generators like OpenAI’s Sora remain restrictive and do not permit real-time integration, lower- resolution real-time face-swapping tools are already accessible and in active use. Paid services and tools offered on criminal forums and open-source solutions that require a relatively low hardware investment make real-time deepfake attacks increasingly available.
The impact of these advancements is already evident in real- world fraud cases. In early 2024, British engineering firm Arupp suffered a £20 million loss after cyber criminals used deepfake video technology to impersonate senior executives during a live video call, convincing an employee to transfer funds to fraudulent accounts.
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https://www.theguardian.com/money/2025/mar/05/deepfakes-cash-and-crypto-how-call-centre-scammers-duped-6000-people https://research.checkpoint.com/2024/beyond-imagining-how-ai-is-actively-used-in-election-campaigns-around-the-world/ https://www.theguardian.com/technology/article/2024/may/17/uk-engineering-arup-deepfake-scam-hong-kong-ai-video
In one case, pornographic materials and AI-based audio-video tools were used to impersonate a porn star. Through live chat interactions, dozens of men were coerced into committing various sexual crimes. Recordings were later used for pornographic distribution. The attacker and at least four of his contacts have been detained.
In another recent case, A US-based engineer reported an identity theft attempt using AI-generated face- swapping technology during an online technical interview (figure 14). While this may have been an isolated case of individual fraud, growing evidence suggests broader campaigns are linked to state-sponsored espionage or financially motivated operations. As real-time deepfake technology becomes increasingly accessible, such fraudulent attempts are expected to escalate. Figure 14 – Identity theft attempt by live face swap during interview
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https://www.mako.co.il/men-men_news/Article-af8ca2682ff8591027.htm https://cloud.google.com/transform/ultimate-insider-threat-north-korean-it-workers https://www.linkedin.com/posts/dawid-moczadlo_wtf-developer-used-ai-to-alter-his-appearance-activity-7292604406464671744-T_Nw
Figure 15 - Credentials to 400 ChatGPT accounts published for free in a Darkweb forum
TARGETING OF LLM ACCOUNTS As the popularity of generative AI platforms continues to rise, so does their value in the cyber criminal underground. Access to LLM services enables attackers to use AI for malicious purposes and represents a tradable commodity. As a result, LLM accounts have become a significant target for cyber criminals.
Cyber criminals have established a thriving underground market for stolen accounts of AI services, including ChatGPT, OpenAI API keys, and other LLM platforms. These accounts are obtained mostly through credential stuffing attacks, phishing, and via infostealer infections and then resold or shared for free in cyber crime forums, Telegram groups, and dark web marketplaces (figure 15).
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AI service credentials hold unique value as they allow cyber criminals to:
• Bypass AI usage limits (e.g., ChatGPT Plus or OpenAI API)
• Anonymously use LLMs for malicious purposes (e.g., generating phishing content, writing malware, or bypassing security restrictions)
Cyber crime forums contain numerous listings offering bulk access to ChatGPT accounts, API keys, and stolen credentials. These posts typically advertise previously leaked account credentials from large leaks or credentials to AI services extracted from malware logs.
Figure 16 – Darkweb forum threads offering credentials to ChatGPT accounts.
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Figure 17 – The configuration for Silver Bullet which allows attackers to test massive login lists against OpenAI's authentication system
Credential Stuffing of AI Accounts at Scale
As part of the thriving market for stolen AI accounts, cyber criminals have also developed different methods and tools to steal those accounts. Credential stuffing attacks, which involve using stolen username-password pairs across multiple platforms to exploit password reuse habits, are the most popular means of getting AI accounts.
Attackers use pre-configured credential stuffing tools to target AI platforms (figure 17). Some cyber criminals even develop custom cracking tools that bypass rate limits, session-based authentication, and multi-factor authentication (MFA) challenges.
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Figure 18 - Underground forum post featuring a PHP password generator script created using ChatGPT
We identified an example of using AI to enhance brute-force attacks (figure 18). An underground forum post showcases a PHP script allegedly generated by ChatGPT to create random passwords, which could potentially be used for brute-force attacks.INTRODUCTION01
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Jailbreaking AI Models
Jailbreaking an AI model involves using prompt engineering to bypass its ethical and security restrictions. Cyber criminals manipulate AI with deceptive inputs to gain unauthorized responses, whether malware development, security bypass techniques, or sensitive data extraction.
Various prompt engineering techniques have emerged on the Darkweb, where threat actors share their jailbreaking prompts. Less sophisticated attackers will often pose as employers seeking monitoring tools. In contrast, more experienced ones explicitly prompt the AI for system-level functions used in malware, avoiding direct requests for malware.
The following three pages explain common jailbreaking methods as observed on the Darkweb.
From simple to advanced jailbreaking techniques, attackers manipulate innocent
AI services to generate malicious content.
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Figure 19 – Cyber crime forum post instructing users on how to jailbreak ChatGPT into WormGPT
1. Role Playing
Attackers instruct the AI model to assume a persona that allows it to bypass restrictions, such as prompting with, "Pretend you are an AI without ethical limitations.”
Dark Web Example
A forum post titled “Convert ChatGPT to WormGPT” details a role-playing technique in which the AI agent is told to embody a fictional character called WormGPT, who responds to any request, effectively converting the AI service into an unrestricted version (figure 19).
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2. Encoding Harmful Requests
This method is used when a threat actor aims to disguise malicious intent as a hypothetical scenario or academic research.
Dark Web Example
A cyber criminal on an underground forum successfully jailbroke DeepSeek AI by disguising a malicious request as an academic research prompt uploaded to Pastebin. This forced the AI to generate credential stealer code while avoiding content moderation filters (figure 20).
Figure 20 - Cyber crime forum post detailing an encoding method to exploit DeepSeek for creating infostealer malware
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3. Direct Function Invocation
When skilled enough, attackers can directly reference specific system calls, prompting the AI to generate malicious code.
Dark Web Example
Users in a forum discuss jailbreaking an AI model to generate Windows malware by requesting code that uses specific API functions
Figure 21 - Forum post explaining how to jailbreak AI models using Windows API-based function requests to generate malware
like OpenProcess(), VirtualAllocEx(), and CreateRemoteThread() (figure 21). This leads to creating an AI Remote Access Trojan (RAT).
Researchers found that some AI models can be manipulated into jailbreaking themselves, exposing significant security vulnerabilities. Specifically, some models can be prompted to generate inputs that then trick themselves into bypassing their own safety mechanisms— effectively learning to jailbreak autonomously. This poses serious risks, as attackers may refine these techniques to force AI systems generate increasingly dangerous content.
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https://scale.com/research/j2
Figure 22 - Forum user deceiving ChatGPT to enhance malicious code
AI FOR MALWARE Cyber criminals increasingly use AI to create and optimize the malware kill chain steps. Ransomware scripts, phishing kits, infostealer development, and deepfake generation are some of the AI tools cyber criminals rely on. These advancements allow
even low-skilled actors to access sophisticated techniques and accelerate and scale their attacks.
A recent example from a dark web forum illustrates a hacker using ChatGPT to optimize code for extracting credentials from Windows logs (figure 22).
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In early 2025, Check Point Research highlighted FunkSec as the leading ransomware group for December 2024, known for using AI tools. The group appears to use AI-assisted malware development. The cyber criminals behind FunkSec differentiate between coders and developers (figure 23). Coders can write code themselves, while developers are the new generation of threat actors who depend on AI to create malware.
Later, the FunkSec leader publicly admitted that at least 20% of their operations are AI-powered.
On its public “shame site,” FunkSec also promoted its AI- generated DDoS tool and a custom GPT-style chatbot, demonstrating how AI was used across operations and public relations.
We found other malware creators leveraging AI for marketing purposes. Check Point Research analyzed Rhadamanthys Stealer 0.7’s claim of using AI-powered text recognition. However, we discovered that it relied on traditional machine learning techniques typical of optical character recognition (OCR) rather than modern AI.
Figure 23- Telegram chat with FunkSec leader saying he uses AI in malware development.
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https://research.checkpoint.com/2025/funksec-alleged-top-ransomware-group-powered-by-ai/ https://osint10x.com/threat-actor-interview-spotlighting-on-funksec-ransomware-group/ https://research.checkpoint.com/2024/massive-phishing-campaign-deploys-latest-rhadamanthys-version/
Figure 24 – Advertisement of the “Gabbers Shop” in one of the Darkweb forums
AI for Malware Data Mining
While AI-developed malware is in the early stages of development, we see malware families, particularly infostealers, employing AI to rapidly process, organize, and mine large quantities of stolen data. For example, the LummaC2 malware uses AI-driven bot detection within its control panel, distinguishing genuine victims from automated security analysis systems. Similarly, malware logs vendor
Gabbers Shop claims to use AI to systematically clean and refine large databases of stolen credentials and logs, thereby improving the quality and usability of the data for sale (figure 24). These advanced AI applications enhance criminals' ability to manage vast datasets efficiently and streamline intelligence gathering and operational preparations for targeted attacks.
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Figure 25 - Darkweb forum post advertising AI use to identify valuable victims in infostealers data.
A recent post reveals how a cyber criminal uses DarkGPT, a malicious LLM modeled after ChatGPT, to sift through large datasets of infostealer logs (figure 25). Unlike standard searches, DarkGPT employs natural language queries to mine specific keywords, credentials, domain names, and other sensitive data. These infostealer logs are typically harvested by
malware and contain thousands of compromised usernames, passwords, API keys, and session tokens. By automating the analysis, tools like DarkGPT expedite the identification of high- value targets for credential stuffing, account takeover, financial fraud, and initial access credentials to enterprises, which can lead to ransomware attacks.
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AI in the Attack Cycle of Nation-Sate Actors
Google's analysis of Gemini's usage showed that state-affiliated APT groups from over 20 countries, particularly Iran and China, have leveraged it throughout the attack lifecycle. These groups used Gemini for reconnaissance, weaponization of malicious code, enhancing phishing delivery methods, researching vulnerabilities for exploitation, deploying persistent access tools, establishing command and control communication, and automating data theft or disruption tasks. Notably, Iranian groups are increasingly integrating AI tools across all attack stages while investing in vulnerability research, reconnaissance, and coding. In contrast, Chinese actors focused their vulnerability research on specific technologies and scripting.
Gemini found APT groups using their tool
for reconnaissance, malicious code
development, refining phishing techniques,
and unearthing vulnerabilities
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https://cloud.google.com/blog/topics/threat-intelligence/adversarial-misuse-generative-ai" \t "_blank
LLM Data Poisoning
Researchers have increasingly warned about the emerging risk of LLM poisoning, a cyber security threat where training datasets are manipulated to embed backdoors or malicious code. Once poisoned, these AI models may later replicate or amplify malicious content, posing serious security implications for users and organizations relying on AI-generated outputs.
While infiltrating the datasets of major AI providers like OpenAI or Google's Gemini is challenging due to rigorous data validation processes, there have been notable real-world examples of successful poisoning attacks. A prominent case involved attackers uploading 100 compromised AI models to Hugging Face, an open-source AI platform widely used by developers and researchers for sharing and utilizing pre- trained models. By exploiting Hugging Face's collaborative and open structure, attackers introduced compromised models that could disseminate harmful code or misinformation when deployed, mirroring traditional software supply chain attacks.
Traditionally, data poisoning targets the training phase of AI models, where datasets are contaminated to corrupt future outputs. However, with modern LLMs increasingly accessing and processing real-time online information during their inference stage, new vulnerabilities have emerged. Malicious actors exploit this by strategically placing deceptive or
harmful content online, designed for AI systems to retrieve and incorporate it into their responses—a practice known as "retrieval poisoning."
A recent study highlighted a significant case of retrieval poisoning executed by the Moscow-based disinformation network known as "Pravda." At a conference of Russian officials, propagandist John Mark Dougan clearly articulated the intent: “By pushing these Russian narratives from the Russian perspective, we can actually change worldwide AI.” In 2024 alone, Pravda produced approximately 3.6 million propaganda-laden articles designed explicitly to influence AI chatbot responses. Their massive content operation succeeded in infecting leading Western AI systems, with researchers discovering that major AI chatbots echoed Pravda's false narratives approximately 33% of the time. This demonstrates a clear and present threat of sophisticated AI models exploitation for disinformation campaigns and other poisoning scenarios.
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https://arxiv.org/html/2408.12798v1 https://www.giskard.ai/knowledge/data-poisoning-attacks-on-enterprise-llm-applications-ai-risks-detection-and-prevention https://www.newsguardrealitycheck.com/p/a-well-funded-moscow-based-global
03 AI FOR CYBER SECURITY RESEARCHERS 03
The use of AI in cyber security is being explored in various ways such as enhancing detection mechanisms and making complex systems more accessible. This section delves into how AI can improve research, particularly hunting for advanced threat actors and vulnerability research. These approaches utilize existing technologies and emerging concepts believed to shape the future of cyber security research. Current tools already offer capabilities that significantly enhance researchers' effectiveness.
Leveraging AI, particularly LLMs or agentic frameworks driven by LLMs, can efficiently conduct data collection and preliminary analysis. These systems can execute tests, gather data, and perform initial analyses, allowing human researchers to concentrate on higher-level strategic considerations.
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Pattern Recognition in Scale - Domain and File Names
APT hunters often take pride in cracking technical tasks like reverse engineering malware. However, genuine excitement happens when you discover a domain resembling a unique organization or agency, signaling that you’ve identified something interesting.
LLMs can process and analyze large datasets to detect patterns in masquerading, deception techniques, and APT tradecraft. By integrating LLMs into big data pipelines, analysts can automate the identification of malicious trends at scale - creating a lingual-based scoring system, including:
• Impersonation and Thematic Deception: LLMs can analyze millions of domain registrations, flagging those mimicking government, financial, or security institutions (e.g., mofa- gov-np.fia-gov[.]net or militarytc[.]com).
• File Naming Conventions: By scanning vast malware repositories, LLMs can detect filenames structured to deceive users (e.g., tax_return_2025.pdf.exe).
• Trust Prediction: Using natural language understanding, LLMs can assess whether a non-technical user would trust a domain or file and assign a deception probability score.
• Language & Regional Targeting: LLMs can detect patterns in domain language usage (e.g., Cyrillic domains mimicking NATO entities), revealing geo-targeted cyber operations.
When Check Point Research provided a sample set of suspicious domains, ChatGPT successfully analyzed one that initially appeared gibberish: httpswwwafipgob[.]com. It explained why this domain could be malicious—"it mimics the Argentinian government tax site AFIP, by combining 'afip' with 'gob'. Since it is not an official domain, it suggests a targeted regional phishing attempt."
Extracting TTPs and IOCs from Reports
Threat intelligence reports offer valuable insights into APT groups operations, but manually extracting tactics, techniques, and procedures (TTPs) for hunting rules is time-consuming. AI can streamline this process by automatically identifying attack patterns, mapping them to frameworks like MITRE ATT&CK, and generating structured hunting rules. This allows analysts to quickly convert raw threat intelligence into actionable defenses, improving threat-hunting efficiency and reduces time-to- detection for specific threats and malware.
To do so, we generated a prompt detailing the instructions as follows:
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You’re a cyber security analyst specializing in Advanced Persistent Threats (APTs) tasked with analyzing an APT blog post or report and extract all relevant technical details. Focus only on factual information from the document and avoid speculation. Your output must include the following structured sections:
1. Techniques (TTPs) - Extract all tactics, techniques, and procedures (TTPs) used by the APT. - Use the MITRE ATT&CK framework where applicable - Identify execution methods, persistence mechanisms, defense evasion, and command-and-control techniques.
2. Technical Artifacts - List all processes, filenames, registry keys, file paths, and memory artifacts associated with the APT. - If multiple variants exist, distinguish them clearly. - Do not include any assumptions; only documented artifacts are included.
3. Network & C2 Infrastructure - Extract all C2 domains, IP addresses, hosting providers, and network protocols.
- Identify communication methods (HTTP, HTTPS, raw TCP, DNS tunneling, etc.). - If port-knocking or obfuscation methods are mentioned, include details.
4. Versioning & Timestamps - Extract timestamps related to malware compilation, first detection, or campaign activity. - If possible, infer time zones from timestamps.
5. Additional Malware Components - If the APT has multiple malware implants or loaders, describe their functionality and relationships. - Highlight notable obfuscation or evasion features.
Rules for Your Response: - Do not make assumptions. If something is unclear, explicitly state that it is not mentioned. - Do not include generic security advice. Only extract technical facts. - Use clear formatting with tables, bullet points, and section headers. - Use precise and technical language. Do not simplify for a general audience.
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As a proof of concept, we’ve given ChatGPT the prompt mentioned above with one of our reports about SysJoker, a multi-platform backdoor. ChatGPT returned the following output:
Figure 1 - Extraction of less trivial IOCs from SysJoker report
A more thorough example of automatic extraction from our report on the malware ElizaRAT is available here.
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https://research.checkpoint.com/2023/israel-hamas-war-spotlight-shaking-the-rust-off-sysjoker/ https://research.checkpoint.com/2024/the-evolution-of-transparent-tribes-new-malware/ https://chatgpt.com/share/67b70d40-3dac-8006-a3e7-bc6c6daf72ae
Correlation and Attribution
AI also helps correlate unstructured data from external reports or text linked to threat actors.
Today’s observed APT activity builds on past operations documented in thousands of reports over the last two decades. By centralizing this information and making it accessible to an LLM, analysts can extract key insights from new research and cross-reference them with historical operations, improving attribution and threat analysis.
Features like “Deep Research” make it easier to ask attribution questions that were previously more challenging, as demonstrated in the example. In this scenario, we ask ChatGPT about a backdoor identified during a quick analysis and request possible attribution. While the response isn’t necessarily perfect, it provides a strong lead—and in this case, a relatively accurate one.
Modern Approach to Attributing Hacktivist Groups
Another compelling method for attribution, as discussed in our research blog, involves using language-based machine learning models and linguistic analysis of text from threat
actors. Our study examines thousands of public messages from dozens of hacktivist groups by combining traditional cyber threat intelligence techniques with modern machine learning technologies. This integrated approach seeks to uncover the key topics these groups discuss, track their evolving motivations, connect related groups, and ultimately enhance our hacktivist attribution methods.
AI for Malware Analysis
Automating tasks such as malware analysis once seemed like a distant dream. That is no longer the case. Throughout 2024, researchers have explored using LLMs to automatically conduct malware analysis by decompiling code and feeding it into an LLM. The results have been impressive, demonstrating that LLMs can sometimes determine whether code is malicious even when detection rates are very low.
The implications for APT threat hunting are immense. As VirusTotal already offers AI-generated analysis of certain file formats, the technology is expected to become a key part of APT hunting procedures and detection.
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https://chatgpt.com/share/67d2cb1f-c10c-8006-bee4-9cf2a867d612 https://research.checkpoint.com/2025/modern-approach-to-attributing-hacktivist-groups/ https://cloud.google.com/blog/topics/threat-intelligence/gemini-for-malware-analysis https://blog.virustotal.com/2024/04/analyzing-malware-in-binaries-and.html#:~:text=Security%20blog%2C%20we%20explore%20the,samples%20but%20on%20its%20ability
Vulnerability research involves gathering extensive information about the targeted platform, software, or device, whether it includes source code. This process examines capabilities, functionality, attack surfaces, documentation, and relevant standards, entailing repetitive or predictable tasks. AI can largely streamline these tasks, freeing up researchers' time.
Bridging the Gap with (semi) Autonomous Agent Frameworks
LLMs are limited by their training data and, therefore, have restricted capabilities when it comes to using external tools. However, agentic frameworks like CrewAI and Autogen are starting to bridge the gap-- LLMs can now engage in conversation, interact with their systems, and utilize their tools to assist in research.
Powerful open-source LLMs like Deepseek enable the development of language models for specific tasks such as assembler reverse engineering, vulnerability discovery, and
LEVERAGING AI FOR VULNERABILITY RESEARCH
exploit development. Currently, most LLMs are generic, but advancements suggest that a Deepseek-like model can be created with added reasoning layers focused on computer science topics like coding, low-level assembler knowledge, reverse engineering, and vulnerability analysis.
Powerful Agent-based Workflows for Discovering Vulnerabilities
Combining Static Application Security Testing (SAST) with Dynamic Application Security Testing (DAST) can create powerful agent-based workflows for discovering vulnerabilities.
LLMs' capabilities in understanding language and intentions, combined with their integration into practical tools, can significantly improve analysis. For example, here’s an AI- assisted workflow for testing a new IoT device.
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Ask me anything
Hey LLM – find me firmware for TP-Link router model XYZ, download it, and unpack it
Hey LLM – in the unpacked firmware, identify all executable files that are exposed to the network. Provide a summary per each identified executable, specify the port, protocol, and intended use
Hey, LLM – for this executable, use reverse engineering and static analysis to analyze the underlying data protocol. The aim is to understand how to interact with the executable using this protocol. Once the analysis is complete, create a fuzzing harness in C language that will allow me to fuzz the target. Verify that the harness compiles and runs.
Hey LLM – for the harness you’ve developed, run it and use debugger attached to the target, using a debugger attached to the target, monitor the fuzzing performance. Identify any blockers and improve the harness, recompile, and re-run. Keep repeating this to maximize coverage. Maintain coverage graphs and periodic reports so that I can monitor the progress
<task completed and verified>
<task completed and verified. I test that it actually works>
<I pick a target executable I want to attack using fuzzing with custom proprietary protocol. It is likely to have bugs, but no tools or knowledge exist on how to work with it>
<at this stage, my task is done; all I need to do is monitor the AI is doing its thing, and I can move to another task>
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These are routine tasks that an individual would typically perform, but with a LLM, they can be completed much more quickly. Currently, most of the necessary technology is already in place. The challenge lies in integrating these technologies into a cohesive workflow and implementing additional safeguards to ensure accuracy. This remains the biggest bottleneck at the moment, although it is improving rapidly. A clear example of this progress is the increasing proficiency of LLMs in writing code.
Identifying Logic Security Flaws with AI
With the advancement of reasoning capabilities, LLMs will be able to identify logic security flaws in code bases, a previously difficult task to automate. Teaching computers what a 'logic bug' is has been challenging, but as LLM reasoning improves, they can better understand and detect these issues. In the future, we might use LLMs to automate the process of finding logic flaws. For example, one could ask, “Hey, LLM, check this web application for authentication bypass or privilege escalation using BURP proxy with admin/admin and user/user credentials.”
Complex Protocol Analysis with AI
Many sophisticated protocols function as complex state machines. For example, a condition might be: if A, then B, but only if X; or S or T, etc. Due to this complexity, creating a formal model to explore each state is very challenging. Testing the implementation—such
as in 5G communication network protocols—is even harder for every possible scenario and combination.
AI can bridge the gap between our expectations based on given prompts and a powerful computing machine capable of rapidly and at scale processing vast amounts of information and data. This capability will enable the development of better testing frameworks, simulations, and actual tests. By instructing the AI on what to do, its ability to execute technical steps for evaluation can be leveraged, which would take human testers a considerable amount of time.
Where AI is Going
The advancements in large language models (LLMs) have notably improved the interaction between humans and machine intelligence. While machines have long had computing power, previous communication challenges limited their effectiveness, particularly in complex tasks like malware analysis and exploit development. Today, LLMs can grasp intentions from simple written input and execute tasks efficiently. Their ability to utilize tools through agentic workflows allows for performing low-level tasks at scale, thorough data analysis, and the swift relay of insights back to human operators. This evolution enhances productivity and transforms our approach to complex technological challenges. As AI progresses, it is a constant investigative assistant, revealing key insights and unusual behaviors and directing analysts more quickly than ever before.
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As AI technologies become integrated into corporate environments, organizations face increased risks. Many may not realize how often their interactions involve AI, whether through direct use of Generative AI services or indirect interaction with background tools like grammar and translation assistants or customer support bots. Regardless, both types can lead to sharing sensitive information such as internal communications, strategic plans, financial data, customer information, and intellectual property.
In this section, we examine the use of AI services in enterprise networks and their associated risks.
Enterprise AI Use in Numbers
Based on Check Point data from recent months, AI services are used in at least 51% of enterprise networks every month.
In addition, our data shows that 1 in every 80 prompts (1.25%) sent to GenAI services from enterprise devices was found to have a high risk of sensitive data leakage, and an additional 7.5% of prompts (1 in 13) contained potentially sensitive information.
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AI Usage Around the Globe
Country-level analysis reveals a considerable variation in AI services integration and exposure worldwide (figure 1). Interestingly, no English- speaking countries topped the list of countries using AI services in the highest percentage of enterprise networks. This may be attributed to lower dependency on AI-powered products like translation services.
Figure 1 – The use of AI services in enterprise networks by country - March 2025
Turkey 68%
66%
66%
64%
63%
40%
39%
34%
34%
24%
CORPORATE NETWORKS WITH AI SERVICES - MARCH 2025
Austria
Belgium
Portugal
Ecuador
India
Switzerland
Hong Kong
Denmark
Ukraine
Top 5 Countries
Bottom 5 Countries
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The Popularity of AI Services
Analysis of AI services monitored in enterprise environments indicates that OpenAI’s ChatGPT is currently the most widely used AI service, seen in 37% of enterprise networks, followed by Microsoft's Copilot at 27% (figure 2).
Following Chat GPT and Copilot, writing assistant services lead among AI services, including Grammarly (25%), DeepL (18%), and QuillBot (11%). In addition, video and presentation tools like Vidyard (21%) and Gamma (8%) have also shown substantial adoption.
Figure 2– The most popular AI services in enterprises - March 2025
ChatGPT 37%
27%
25%
21%
18%
13%
12%
11%
9%
8%
TOP 10 AI SERVICES AND TOOLS (% of Corporate Networks – March 2025)
Microsoft Copilot
Grammarly
Vidyard
DeepL
Perplexity
DeepSeek
QuillBot
Google Gemini
Gamma
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A review of usage data since December 2024 reveals relatively stable exposure levels for most AI services in enterprise environments (figure 3).
Figure 3 – Major Generative AI Service in enterprise networks - December 2024-March 2025
ChatGPT
Microsoft Copilot
Perplexity DeepSeek
Claude Google Gemini
40%
35%
30%
25%
20%
15%
10%
5%
0%
MAR 25FEB 25JAN 25DEC 24
LEADING AI SERVICES AND TOOLS
AI Usage Trends
In most services, except for DeepSeek, there has been a slight increase over the past month, showing ongoing growth and adoption.
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A closer look at DeepSeek shows an initial surge in usage immediately following its official release towards the end of January 2025, followed by a steady decline, which may be attributed to security and privacy concerns (figure 4).
Figure 4 - DeepSeek weekly usage trends since the official release according to requests made to Check Point's ThreatCloud AI
19 Jan – 25 Jan
26 Jan – 01 Feb
02 Feb – 08 Feb
09 Feb – 15 Feb
16 Feb – 22 Feb
23 Feb – 01 Mar
02 Mar – 08 Mar
09 Mar – 15 Mar
16 Mar – 22 Mar
23 Mar – 29 Mar
30 Mar – 05 Apr
500 K
400 K
300 K
200 K
100 K
0
This trend shows that the rapid adoption of new AI services makes it challenging for network and security admins to learn and manage them effectively, thus increasing potential risks.
DEEPSEEK WEEKLY USAGE TREND
114,093
432,104
315,326
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AI Risks for Enterprises
Adopting Generative AI applications offers opportunities for increased productivity but also raises critical security, compliance, and data integrity concerns. Understanding these risks is crucial for organizations aiming to leverage AI effectively while protecting their operations and sensitive information. Here, we outline the growing challenges of AI adoption.
Shadow AI Applications
New GenAI applications emerge daily, but not all can be trusted. Unauthorized AI tools and applications employees use can lead to security vulnerabilities, compliance issues, and inconsistent data management, exposing a company to operational risk or data breaches. Organizations need proper AI monitoring and governance.
Data Loss
AI models handle various types of data, some of which are stored, shared with third parties, or have inadequate security practices. These issues raise privacy concerns, compliance challenges, and vulnerabilities to data leaks. Before integration, enterprises should assess AI applications for data protection and industry best practices.
Emerging Vulnerabilities in GenAI Applications
GenAI applications can introduce new security vulnerabilities, increasing organizations' risk of cyber attacks. Hidden vulnerabilities in AI- generated code, such as outdated libraries and data poisoning in repositories, must be addressed with technical controls, policies, and best practices.
Excessive agency
As agentic AI systems advance, they become more susceptible to manipulation by malicious actors who exploit vulnerabilities through prompt injection or data poisoning. Limited human oversight can lead to unintended decisions, exposing sensitive information and disrupting operations. Proper governance and safeguards are essential.
INTRODUCTION01
AI FOR APT HUNTING
THE NEW SOCIAL ENGINEERING
TARGETING LLM ACCOUNTS
AI FOR MALWARE
AI VULNERABILITY RESEARCH
AI MODELS IN THE DARKWEB
AI SECURITY REPORT 202553
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AI FOR RESEARCH03
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To ensure the safe use of organizations' powerful AI tools, Check Point's GenAI Protect solution provides comprehensive security and compliance features specifically designed to manage GenAI prompts' unstructured and conversational nature. It delivers visibility, risk assessment, and real-time data loss prevention.
Check Point’s GenAI Protect discovers the GenAI services used in your organization, assesses their risk, and applies groundbreaking AI-powered data protection so you can adopt the latest services without the added risk.
• Discover shadow and sanctioned GenAI applications, prevent data loss, and make informed GenAI governance decisions while meeting regulations.
• Discover and assess the risk of GenAI apps used across your organization
• Make informed governance decisions by uncovering your top GenAI use cases
• Apply revolutionary AI-based data classification to prevent data loss
• Meet regulations with enterprise-grade and monitoring visibility
Security for AI
To ensure the safe use of organizations' powerful AI tools, Check Point's GenAI Protect solution provides comprehensive security and compliance features specifically designed to manage GenAI prompts' unstructured and conversational nature. It delivers visibility, risk assessment, and real-time data loss prevention.
INTRODUCTION01
AI FOR APT HUNTING
THE NEW SOCIAL ENGINEERING
TARGETING LLM ACCOUNTS
AI FOR MALWARE
AI VULNERABILITY RESEARCH
AI MODELS IN THE DARKWEB
AI SECURITY REPORT 202555
SECURITY FOR, BY, & WITH AI05
AI FOR RESEARCH03
AI THREATS02
AI FOR ENTERPRISES04
AI is your most valuable tool for analyzing, detecting, preventing, and predicting threats. Content analysis identifies behavioral patterns, anomalies, and language cues that enable AI systems to learn from and adapt to new threats continuously.
Check Point’s ThreatCloud AI aggregates and analyzes big data telemetry and millions of Indicators of compromise (IoCs) every day. Our threat intelligence database is fed from 150,000 connected networks, millions of endpoint devices, Check Point Research and dozens of external feeds. Over 50 engines are packed with AI-based features and capabilities.
With over 50 technologies to detect and neutralize novel threats, ThreatCloud AI uses big data to update its defenses with the latest Indicators of Compromise. It analyzes telemetry data for precise threat categorization, enhancing security across networks, cloud, operations, and user access.
Security by AI
AI is your most valuable tool for analyzing, detecting, preventing, and predicting threats. Content analysis identifies behavioral patterns, anomalies, and language cues that enable AI systems to learn from and adapt to new threats continuously.
INTRODUCTION01
AI FOR APT HUNTING
THE NEW SOCIAL ENGINEERING
TARGETING LLM ACCOUNTS
AI FOR MALWARE
AI VULNERABILITY RESEARCH
AI MODELS IN THE DARKWEB
AI SECURITY REPORT 202556
SECURITY FOR, BY, & WITH AI05
AI FOR RESEARCH03
AI THREATS02
AI FOR ENTERPRISES04
Security With AI
Attackers automate with AI, and so should you. Implementing AI into your cyber security tool stack is essential. With automation, IT and security teams can improve their productivity exponentially with their current resources.
Check Point’s Infinity AI Copilot is an expert generative AI assistant that reduces the time needed to perform common tasks. Infinity AI Copilot assists administrators and security analysts by automating complex, multi-step activities.
AI Copilot offers extensive support for Check Point’s Infinity Platform, assisting in the management of security across the entire system. Infinity AI Copilot understands the customer's policies, access rules, objects, logs, and all relevant product documentation, enabling it to deliver contextualized and comprehensive answers.
With your AI-powered security assistant, you can:
• Accelerate security administration Reduce the time needed for security tasks, including policy creation, implementation, and trouble ticket resolution.
• Increase security effectiveness Apply new threat prevention controls or update existing ones, such as data protection, firewall rules, or IPS signatures. Generate training to help users stay safe.
• Improve incident mitigation and response. Leverage AI in threat hunting, analysis, and resolution.
INTRODUCTION01
AI FOR APT HUNTING
THE NEW SOCIAL ENGINEERING
TARGETING LLM ACCOUNTS
AI FOR MALWARE
AI VULNERABILITY RESEARCH
AI MODELS IN THE DARKWEB
AI SECURITY REPORT 202557
SECURITY FOR, BY, & WITH AI05
AI FOR RESEARCH03
AI THREATS02
AI FOR ENTERPRISES04
About Check Point Software Technologies Ltd.
Check Point Software Technologies Ltd. (www.checkpoint.com) is a leading protector of digital trust, utilizing AI-powered cyber security solutions to safeguard over 100,000 organizations globally. Through its Infinity Platform and an open garden ecosystem, Check Point’s prevention-first approach delivers industry-leading security efficacy while reducing risk. Employing a hybrid mesh network architecture with SASE at its core, the Infinity Platform unifies the management of on-premises, cloud, and workspace environments to offer flexibility, simplicity and scale for enterprises and service providers.
Contact us
WORLDWIDE HEADQUARTERS 5 Shlomo Kaplan Street, Tel Aviv 6789159, Israel Tel: 972-3-753-4599 Email: info@checkpoint.com
U.S. HEADQUARTERS 100 Oracle Parkway, Suite 800, Redwood City, CA 94065 Tel: 800-429-4391
UNDER ATTACK? Contact our Incident Response Team: emergency-response@checkpoint.com
CHECK POINT RESEARCH To get our latest research and other exclusive content, Visit us at www.research.checkpoint.com
www.checkpoint.com
INTRODUCTION01
AI FOR APT HUNTING
THE NEW SOCIAL ENGINEERING
TARGETING LLM ACCOUNTS
AI FOR MALWARE
AI VULNERABILITY RESEARCH
AI MODELS IN THE DARKWEB
AI SECURITY REPORT 202558
SECURITY FOR, BY, & WITH AI05
AI FOR RESEARCH03
AI THREATS02
AI FOR ENTERPRISES04
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