Phishing involves fraudulent attempts to obtain sensitive information such as usernames, passwords, and credit card details by masquerading as a trustworthy entity in electronic communication.
Attackers often use email spoofing to send messages that appear to come from reputable sources like banks or trusted companies. These emails may contain links to malicious websites that mimic legitimate login pages.
Attackers send emails appearing to be from a reputable source, like a bank, containing links to malicious websites that mimic legitimate login pages:
Clicking the link directs the user to a fraudulent site where their credentials can be harvested.
AI enables attackers to create highly personalized phishing emails that are more convincing:
An AI model analyzes a target's social media profiles to craft a phishing email:
Vishing is a type of phishing attack that uses phone calls or voice messages to deceive individuals into revealing confidential information or performing actions that compromise security.
An attacker uses Voice over Internet Protocol (VoIP) technology to spoof the caller ID, making it appear as if the call is coming from a legitimate bank. The attacker calls the victim and poses as a bank representative, stating:
"This is [Bank Name] security department. We've detected suspicious activity on your account. To secure your funds, please verify your account number, PIN, and recent transaction details."
Believing the call is authentic due to the recognizable caller ID and urgent tone, the victim provides the requested information. The attacker then uses these details to access the victim's bank account, transfer funds, or make unauthorized purchases.
AI enhances vishing through:
An attacker uses AI to clone a CEO's voice and leaves a voicemail for an employee:
"Hi, this is [CEO's Name]. I'm tied up in a meeting, but I need you to process an urgent wire transfer to our new client. Details are in your email."
Spear phishing is a more refined form of phishing that targets specific individuals or organizations, using personalized information to increase credibility.
Example: An attacker researches an employee on social media and discovers they recently attended a cybersecurity conference. The attacker then sends an email:
The personalized context increases the likelihood of the employee clicking the link.
Whaling attacks focus on high-profile individuals like CEOs or CFOs, aiming to exploit their access to sensitive information.
Example: An attacker impersonates a CEO and sends an email to the finance department:
The sense of urgency and authority pressures the recipient to comply without verification.
Pretexting involves creating a fictitious scenario to trick victims into revealing confidential information.
Example:
An attacker calls an employee, claiming to be from the IT help desk:
Believing the request is legitimate, the employee may disclose their username and password.
Baiting uses the promise of something desirable to lure victims into a trap.
Example:
An attacker leaves USB flash drives labeled "Salary Summary Q1" in a company's parking lot. Curious employees pick up the drives and insert them into their computers, unknowingly installing malware that grants the attacker access to the corporate network.
These techniques involve gaining unauthorized physical access to secure areas by exploiting human trust.
Example: An attacker carrying heavy boxes approaches a secure door. When an employee opens the door, the attacker asks them to hold it, gaining access without proper authentication.
Viruses attach themselves to clean files and spread to other files.
A macro virus embedded in a Word document activates when the document is opened, infecting other documents.
Worms exploit vulnerabilities to infect systems without user intervention.
The SQL Slammer worm exploited a buffer overflow vulnerability in Microsoft's SQL Server, causing widespread network congestion.
AI enhances malware capabilities:
A worm uses reinforcement learning to identify the most effective exploit paths within a network, adapting its propagation strategy to maximize infection rates while minimizing detection.
Trojans appear as legitimate programs but perform malicious activities when executed.
Example: A downloaded game includes a Trojan that, when installed, opens a backdoor on the system using port 4444. The attacker can now remotely access and control the system.
Ransomware encrypts user data and demands payment for the decryption key.
Example: WannaCry exploited SMB protocol vulnerabilities to spread rapidly. It encrypted files and displayed a ransom note demanding Bitcoin payment.
AI improves ransomware through:
Ransomware analyzes system files to prioritize encrypting critical assets first, using AI to predict which files are most valuable to the victim.
> Read more about the main ransomware groups
A spyware monitors user activity to collect information.
Example: A spyware application records browser history, keystrokes, and screenshots, sending the data to the attacker.
An adware displays unwanted advertisements.
Example: Adware injects ads into web pages or redirects search queries to advertising sites.
Rootkits modify the operating system to hide malicious processes and files from detection tools.
Example: A kernel-mode rootkit replaces system drivers like ndis.sys to intercept network traffic and hide its presence from tools like Task Manager and antivirus software.
Botnets consist of numerous infected devices (bots) controlled by an attacker (botmaster) to perform coordinated actions.
Example: The Mirai Botnet infected IoT devices like cameras and routers using default credentials. It has been used for DDoS attacks, overwhelming targets with traffic exceeding 1 Tbps.
DoS attacks overwhelm a system's resources, rendering services unavailable.
Example: Attackers send a succession of SYN requests to a target's server, consuming resources by leaving half-open connections. (SYN Flood)
AI refines DoS attacks by:
An AI-driven botnet adjusts packet sizes and intervals to mimic legitimate traffic patterns, evading detection by anomaly-based intrusion prevention systems.
DDoS attacks use multiple compromised systems to amplify the attack.
Example: Botnets send large UDP packets to random ports on the target server, forcing it to check for applications listening on those ports and reply with ICMP "Destination Unreachable," consuming bandwidth. (UDP Flood)
In a MitM attack, hackers secretly relay and possibly alter communications between two parties.
Example: An attacker uses a rogue Wi-Fi hotspot and SSL stripping techniques to downgrade HTTPS connections to HTTP, intercepting sensitive data. (HTTPS Spoofing)
AI enhances MitM attacks through:
An AI system analyzes encrypted traffic to detect patterns that could indicate key reuse, aiding in decrypting communications without the user's knowledge.
In DNS Spoofing (or DNS Poisoning) attacks, hakers alter DNS records to redirect traffic to fraudulent sites.
Example: By injecting forged entries into a DNS server's cache, the domain www.example.com resolves to the attacker's IP address, leading users to a malicious website.
Attackers send falsified ARP messages to associate their MAC address with another host's IP address.
Example: The attacker sends an ARP reply stating that the gateway's IP address maps to their MAC address. Traffic intended for the gateway is sent to the attacker, enabling packet sniffing or manipulation.
Attackers inject malicious SQL statements into input fields to manipulate backend databases.
AI automates the discovery of injection points:
An AI tool scans web applications, learning from responses to craft SQL injection attacks that evade security mechanisms like input sanitization.
> How to detect SQL Injection attacks
XSS attacks involve injecting malicious scripts that execute in a user's browser.
Example: An attacker posts a comment containing on a forum. When other users view the comment, their browsers execute the script, sending their session cookies to the attacker.
AI improves XSS attacks by:
An AI system crafts XSS payloads that adapt to different browser versions and security settings, increasing the success rate of the attack.
CSRF tricks authenticated users into submitting requests without their knowledge.
Example: An attacker crafts a hidden form on their website that submits a POST request to http://bank[.]com/transfer when the page loads. If a user is logged into their bank account, the request transfers funds to the attacker's account.
RFI allows attackers to include and execute remote files through vulnerable scripts.
Attackers attempt all possible combinations to discover passwords.
Example: Using tools like Hydra, an attacker can target an SSH server to get the passwords.
AI enhances efficiency:
A model like PassGAN generates password guesses based on patterns from leaked databases, significantly reducing the time required to crack passwords.
Attackers use a list of common passwords to guess user credentials.
Multiple password lists can be found online containing most common passwords like password, 123456, qwerty.
The attacker can automate login attempts using these passwords against multiple accounts.
Attackers use username and password pairs from data breaches to access accounts on other services.
Example: Credentials from a compromised e-commerce site are used to attempt logins on banking websites. Success relies on users reusing passwords across services.
Keyloggers capture keystrokes to obtain sensitive information like passwords and credit card numbers.
A software keylogger runs silently in the background, logging all keystrokes and periodically sending logs to the attacker's server.
In password spraying attacks, hackers try a small number of commonly used passwords across many accounts to avoid account lockouts.
Example: The attacker attempts passwords like Welcome1! or Password2023 on all user accounts in an organization.
Attackers capture data transmitted over unencrypted Wi-Fi networks.
Example: Using Aircrack-ng, an attacker captures packets from an open Wi-Fi network to intercept email logins sent in clear text.
Vulnerabilities in Bluetooth protocols allow attackers to connect without authorization.
Example: The hacker exploits Bluetooth implementation flaws to execute code remotely on unpatched devices. (BlueBorne Attack)
Malicious applications or compromised legitimate apps can infect mobile devices.
Example: A trojanized version of a popular app requests excessive permissions, allowing it to read messages, access contacts, and transmit data to the attacker.
IoT devices often lack robust security measures, making them easy targets.
Example: An attacker accesses a smart thermostat with default credentials, using it as a pivot point to scan and attack other devices on the network.
Compromised IoT devices contribute to powerful botnets.
Example: The Reaper Botnet exploited vulnerabilities in IoT devices to build a network capable of launching high-volume DDoS attacks.
Attackers target misconfigured or vulnerable cloud services.
Example: An incorrectly configured Amazon S3 bucket allows public read/write access, exposing sensitive data.
Misconfigurations lead to unauthorized access or privilege escalation.
Example: An attacker exploits overly permissive IAM roles in AWS to escalate privileges and gain control over cloud resources.
Today, AI scans third-party software for exploitable flaws and machine learning automates the insertion of malicious code into complex systems, making it easier for the attacker to compromise elements in the supply chain to infiltrate targets.
Zero-day exploits take advantage of software vulnerabilities unknown to the vendor.
Example: The Stuxnet Worm utilized multiple zero-day vulnerabilities to target and damage Iran's nuclear centrifuges.
Breaking Encryption Algorithms
Attackers exploit weaknesses in encryption protocols or implementations.
Example: The Padding Oracle Attack exploits padding errors in cryptographic operations to decrypt ciphertext without the key.
Man-in-the-middle attacks compromise SSL/TLS by taking advantage of protocol weaknesses.
Example: The POODLE Attack downgrades TLS connections to SSL 3.0, which is vulnerable to certain types of attacks, allowing the attacker to decrypt session cookies.
Attackers physically alter devices to introduce vulnerabilities.
Example: Installing a malicious PCIe card that provides unauthorized access to system memory and data.
Unencrypted devices pose significant risks if lost or stolen.
Example: A lost USB drive containing unencrypted customer data leads to a data breach when found by an unauthorized individual.
Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized many industries, including cybersecurity. While AI provides powerful tools for defense, attackers are increasingly leveraging AI to enhance their attack methodologies.
The integration of AI into cyber attack techniques significantly enhances the sophistication and effectiveness of threats. Attackers leverage AI for automation, adaptability, and improved success rates, challenging traditional security measures.
By understanding both traditional attack techniques and the impact of AI, organizations can develop robust strategies to protect against evolving cyber threats.
Vectra AI leverages advanced artificial intelligence and machine learning to detect sophisticated cyber threats across the attack techniques discussed. By continuously monitoring network traffic, user behavior, and system interactions, Vectra AI's platform identifies anomalies and malicious activities in real-time. It detects signs of social engineering, AI-enhanced malware, network-based attacks, web application exploits, credential abuse, advanced persistent threats, insider threats, supply chain compromises, and IoT vulnerabilities.
Using AI-driven analytics, Vectra AI can recognize patterns and deviations that traditional security tools might miss, even when attackers employ AI to enhance their methods. This proactive approach enables organizations to quickly identify and respond to both conventional and AI-powered cyber attacks, significantly improving their security posture in an evolving threat landscape.