Printer issues are very common, but searching Google for help may get you into more trouble than you'd expect.

Anyone who has ever used a printer likely has had a frustrating experience at some point. There always seems to be some kind of issue with the software not responding, paper getting jammed or one of many other possible failures.

When people need help, they often turn to Google (and now AI) to look for an answer. This is where scammers come in, preying on unsuspecting and irate users ready to throw their printer out the window.

After clicking on a malicious Google ad, victims are redirected to a fraudulent site often using official brand names and logos. The crooks’ end goal is to get people to call them, and they achieve that by tricking them with fake printer drivers that always fail to install.

In this blog post, we review how this scam works and how to stay away from it.

**Malicious Search Ads**

Two of the most popular printer brands are HP and Canon. If you were to Google for help related to either of those brands right now, you would likely see sponsored results at the top of the search results page.

Unfortunately, in the majority of cases these ads are not from trusted providers but instead from tech support scammers. In the image below, you can see 4 ads shown for the query ‘_hp printer help_‘. It’s only after those that the official HP website appears.

If you were to say that consumers stand no chance, you’d be right. Unless you clicked on the official (organic search results), you’d end up getting scammed.

The list of sites includes:

megadrive\[.\]solutions  
geeksprosoftwareprints\[.\]org  
select-easy123print\[.\]com  
printcaretech\[.\]com

**The driver scam**

A driver is a software program that your computer uses to talk to physical hardware (i.e. your printer). In the early Microsoft Windows days, drivers were very important to get printers, monitors and other peripherals working. Today, the operating system is usually good at detecting new hardware and installing the required drivers automatically. There are some exceptions, not to mention that some manufacturers like to package additional software with their drivers.

After clicking on a malicious ad, the website instructs you to enter your printer’s model number in order to download the required driver, which it proceeds to “install”. This is entirely fake, and the only thing the website displays is a recorded animation that will always end up with the same error message.

This type of error is very similar to those seen in the “Microsoft tech support scam”, typically done via a browser hijack. Scammers want to scare and then get their victims to contact them directly, via phone or live chat.

**Remote access and extortion**

There are many people that fall for these types of scams and entire armies of tech support agents working in poor conditions ready to defraud them. The script is usually standard across scams, with the support agent impersonating a popular brand and requesting personal information from the victim.

It is quite common for scammers to request and be granted remote access to the user’s computer. This gives them leverage to do a number of things, such as stealing data, locking the machine or even using it to log into the victim’s bank account.

This is why it is so important to be extremely cautious with online search ads, and search results in general. Browser extensions such as Malwarebytes Browser Guard will block ads but also the scam or malware sites associated with these schemes.

This won’t help with your printer issues, but at least it’ll save you the trouble of being defrauded. When it comes to such questions, online forums are usually a good place to start, and if you’re lucky to count a computer person in your family, that’s always a good favor to ask for.

 Printer problems? Beware the bogus help 

Ant-Media-Server v2.8.2 is affected by Improper Output Neutralization for Logs. The vulnerability stems from insufficient input sanitization in the logging mechanism. Without proper filtering or validation, user-controllable data, such as identifiers or other sensitive information, can be included in log entries without restrictions.

**Exploitability Metrics**

Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.

Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.

Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.

Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.

User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.

**Vulnerable System Impact Metrics**

Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.

Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).

Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.

**Subsequent System Impact Metrics**

Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.

Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).

Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.

GHSA-2gx6-qrpp-c4p3: Ant-Media-Server vulnerable to Improper Output Neutralization for Logs

A deserialization vulnerability exists in the Stub class of the VarDumper module in Symfony. The vulnerability stems from deficiencies in the original implementation when handling properties with null or uninitialized values. An attacker could construct specific serialized data and use this vulnerability to execute unauthorized code.

GHSA-cg28-v4wq-whv5: Symfony's VarDumper vulnerable to unsafe deserialization

In Symfony, a security vulnerability was identified in the FormLoginAuthenticator component, where it failed to adequately handle cases where the username or password field of a login request is empty. This flaw could lead to various security risks, including improper authentication logic handling or denial of service.

GHSA-7q22-x757-cmgc: Symfony http-security has authentication bypass

moby v25.0.5 is affected by a Race Condition in builder/builder-next/adapters/snapshot/layer.go. The vulnerability could be used to trigger concurrent builds that call the EnsureLayer function resulting in resource leaks/exhaustion.

GHSA-2mj3-vfvx-fc43: Moby Race Condition vulnerability

moby v25.0.3 has a Race Condition vulnerability in the streamformatter package which can be used to trigger multiple concurrent write operations resulting in data corruption or application crashes.

GHSA-gh5c-3h97-2f3q: Moby Race Condition vulnerability

Whether it's detecting fraudulent activity, preventing phishing, or protecting sensitive data, AI is transforming cybersecurity in ridesharing.

Source: Askar Karimullin via Alamy Stock Photo

COMMENTARY

Picture yourself standing on a busy street corner, smartphone in hand. With just a few taps, you summon a car that will arrive in minutes. This seemingly simple action has now become a daily routine for millions of Americans across the country. But as ridesharing apps offer unparalleled convenience, they also face growing concerns about security and data protection. As a result, these platforms are increasingly turning to artificial intelligence (AI) to fortify their defenses and ensure the safety of both riders and drivers.

Beneath the surface of this seamless user experience lies a complex ecosystem of AI algorithms working tirelessly to keep every journey safe and secure. Whether it's detecting fraudulent activity, preventing phishing attempts, or protecting sensitive data, AI is fundamentally transforming the cybersecurity landscape in ridesharing.

**AI-Driven Identity Verification**

Security starts at the very first step of the ridesharing journey — identity verification. Both riders and drivers must be authenticated to ensure a secure experience. However, verifying millions of users poses a substantial challenge. That's where AI steps in as a powerful tool for combating identity fraud.

Driver Authentication: AI-powered facial recognition systems use computer vision to compare selfies taken by drivers with their government-issued IDs. This process ensures that the person behind the wheel matches the registered account. To enhance security, these platforms implement periodic re-verification through biometric checks, preventing fraudulent actors from using stolen accounts to access the platform.

Rider Authentication: Currently, riders are authenticated through basic checks such as validating email addresses, phone numbers, and payment methods. However, the potential for AI in rider verification extends far beyond these initial steps. In the future, AI systems could incorporate more sophisticated predictive modeling to detect anomalies in user activity — for example, unusual patterns in booking history or device usage could flag compromised accounts, enabling platforms to intervene before any security breach occurs.

**Detecting Fraud and Phishing Attacks**

One of the most pervasive threats in digital platforms today is phishing. With the rise of sophisticated phishing schemes aimed at ridesharing users — whether to steal credentials or payment information — ridesharing apps have embraced AI-driven systems to detect and block malicious attempts in real time.

Fraud and Phishing Detection: Fraudsters often exploit vulnerabilities like stolen payment information or fake driver profiles to manipulate the system for unauthorized gains. Meanwhile, phishing campaigns attempt to trick users — both drivers and riders — into revealing sensitive details. AI tackles these threats by:

*   Identifying Suspicious Behavior: AI models flag irregularities, such as unusual login locations, sudden changes in ride patterns, or attempts to manipulate driver or payment profiles.
    
*   Blocking Phishing Attempts: Sophisticated algorithms analyze signals like abnormal contact rates, high cancellation frequencies, and sequential anomalies to detect and prevent phishing schemes.
    
*   Responding Swiftly to Threats: When anomalies are detected, AI systems react in real time by locking compromised accounts, intercepting fraudulent actions, and mitigating risks before they escalate.
    
*   Payment Security: AI also plays a critical role in securing payment transactions. Using machine learning, ridesharing platforms can detect anomalies in payment processing, such as transaction tampering or repeated failed payments, that could indicate fraudulent activity. Payment gateways are closely monitored for suspicious transactions, and any deviations from typical user behavior are flagged for further review.
    

**Real-Time Threat Monitoring**

While preemptive security measures like identity verification and encryption are essential, ridesharing platforms must also continuously monitor for real-time threats during rides. Here, AI-driven systems act as vigilant guardians, ensuring safety throughout the journey.

*   Monitoring for Suspicious Behavior: AI systems monitor ongoing trips, flagging erratic behavior such as deviations from planned routes or excessive speed. By using GPS data, machine learning models can identify unsafe driving patterns and alert both the rider and driver to potential issues. This real-time monitoring not only ensures physical safety but also acts as a safeguard against hijacking or driver impersonation.
    
*   Emergency Response Systems: Ridesharing platforms have integrated AI-enhanced emergency features into their apps, allowing users to access help instantly. One-tap emergency buttons are backed by AI-driven systems that can instantly share real-time ride data, including location and driver information, with authorities or emergency contacts. In addition, AI models can analyze data from encrypted dashcams and provide insights into incidents that require rapid intervention, ensuring that support arrives as quickly as possible.
    

**AI, the Guardian of Ridesharing Security**

Looking ahead, AI will play a pivotal role in enhancing the security and privacy of ridesharing platforms. As the amount of personally identifiable information (PII) grows, AI systems will continue to evolve, strengthening encryption, anomaly detection, and proactive threat monitoring. Machine learning models will not only monitor for emerging cyber threats, such as phishing and fraud, but also predict and flag high-risk behaviors like frequent ride cancellations or erratic driving. By deprioritizing these risky matches, AI ensures a safer experience for both riders and drivers.

With the integration of real-time cyber threat intelligence, AI will adapt to new attack methods, staying one step ahead of cybercriminals. Predictive analytics will help identify potential risks before they escalate, allowing ridesharing platforms to take action early. AI's ability to monitor and mitigate threats in real time, coupled with its capacity for proactive threat prediction, will provide a resilient, adaptive security framework.

As ridesharing services continue to transform urban mobility, the role of AI in ensuring security and privacy will only grow. AI will enable platforms to address increasingly sophisticated cybersecurity challenges, providing a robust foundation for privacy, safety, and trust in a rapidly evolving digital world. Through its continuous innovation, AI will not only make rides more convenient but will also create a safer, more secure environment for users worldwide.

**About the Author**

Machine Learning Engineer, Lyft

Rachita Naik is a Machine Learning Engineer at Lyft in New York with a Master's in Computer Science from Columbia University. Passionate about leveraging machine learning to solve real-world problems, she has developed a diverse skill set through academic projects and professional experience. At Lyft, she works on algorithms to optimally match riders with drivers, enhancing marketplace efficiency and profit. Rachita thrives in fast-paced environments and is always eager to learn new technologies to improve her work.

How AI Is Enhancing Security in Ridesharing

Check Point Research has discovered cybercriminals exploiting the popular Godot Game Engine to deliver malicious software. Discover the techniques used by attackers and how to protect yourself from these threats.

****SUMMARY****

*   **Cybercriminals are exploiting the Godot game engine to deliver malware called GodLoader, targeting multiple platforms like Windows, macOS, and Linux.**

*   **GodLoader hides malicious code in game files, bypassing antivirus detection and compromising over 17,000 devices since June 2024.**

*   **The malware uses sandbox evasion, Microsoft Defender exclusions, and GitHub-hosted repositories to distribute attacks.**

*   **GodLoader’s payloads include RedLine Stealer and cryptocurrency miners, affecting 1.2 million Godot game users.**

*   **The Godot team advises downloading software from trusted sources and avoiding cracked files to stay safe.**

Check Point Research (CPR) has published its latest research on a novel multi-platform technique employed by cybercriminals to exploit the popular open-source game engine, Godot to deliver a newly discovered malicious payload dubbed GodLoader after bypassing traditional security measures.

The concerning aspect is GodLoader’s cross-platform functionality, making it effective on macOS, Windows, Linux, iOS, and Android. Although designed to target Windows, it can be used on Linux and macOS with minimal adjustments.  The malware is, reportedly, distributed via the Stargazers Ghost Network on GitHub, using over 200 repositories and 225 accounts between September and October 2024. 

“The threat actor behind this malware has been utilizing it since June 29, 2024, infecting over 17,000 machines,” and an attack can put 1.2 million users of Godot-developed games at risk, researchers noted in the blog post.

According to CPR’s research, cybercriminals exploit the flexibility of Godot’s scripting language, GDScript and embed malicious code within game assets, executing it when the game is launched. This is a stealthy approach, which enables attackers to bypass antivirus detection and compromise systems without raising alarms.

Further probing revealed that it uses sandbox and virtual machine detection, as well as Microsoft Defender exclusions, to avoid detection. The malware was hosted on Bitbucket.org and distributed across four attack waves, with initial payloads including RedLine Stealer and XMRig cryptocurrency miners.

For your information, Godot is a powerful tool for game development that allows developers to bundle game assets and scripts into .pck files, which contain the game’s resources, including images, sounds, and scripts. By injecting malicious GDScript code into these .pck files, attackers can trick the game engine into executing harmful commands.

As soon as the game loads the infected .pck file, the hidden script springs into action, downloading and deploying additional malware payloads onto the victim’s device.

The attack flow (Via CPR)

****Godot Engine’s Statement****

The Godot Engine development team, in response, has issued a statement, explaining that GodLoader doesn’t exploit a specific weakness in Godot itself because like any programming language (e.g. Python or Ruby) Godot also allows the creation of both good and bad programs. Though the malware exploits Godot’s scripting language (GDScript) to deliver its payload, this doesn’t make Godot inherently unsafe.

The team also noted that it isn’t a one-click exploit because the GodLoader malware tricks users into downloading/executing a seemingly harmless file (typically a .pck file disguised as a software crack). This file wouldn’t work on its own and the attackers also must provide the Godot runtime (.exe file) separately to make it successful. This means users have to take multiple steps to install the malware, making it less likely to be a one-click exploit.

Nonetheless, team Godot emphasizes the importance of good security habits and downloading from trusted sources like official websites, established distribution platforms, or trusted individuals. Windows and macOS users should check for signed executables and notarization by a trusted party and avoid using cracked software as it is a common target for malicious actors.

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Python has emerged as a powerful ally in combating rising cybersecurity threats and tracking cybercrime through tools leveraging…

Python has emerged as a powerful ally in combating rising cybersecurity threats and tracking cybercrime through tools leveraging **threat intelligence**. With its versatility and powerful libraries, experts today can create solutions that run large algorithms and enhance searchability and scalability.

****Unmatched Versatility in Threat Detection****

Cyber threats are evolving rapidly, and tools must stay ahead to counter them effectively. Python stands out with its versatility and a wide range of libraries tailored for cybersecurity. For instance, Scapy allows developers to analyze network packets, identifying suspicious behaviours before they can cause harm. Additionally, Python can be used to create predictive models to detect and mitigate threats using frameworks like TensorFlow.

****Real-life use cases highlight Python’s potential****

Python is incredibly useful in real-world cybersecurity. For network monitoring, tools like **Pyshark** leverage Python to decode and analyze network traffic for anomalies. Automated penetration testing benefits from Python scripts that streamline server and application vulnerability scans. In malware analysis, the Python-based **YARA** library plays a crucial role in identifying patterns of malicious code, making it an invaluable tool for threat detection.

Beyond technical strength, Python’s simplicity makes it easy for cybersecurity teams of different levels of expertise to work together. **Python software development services** are used by developers and analysts alike to easily integrate threat detection into existing systems.   

****Why Python Outshines Other Languages in Cybersecurity**  **

Python isn’t the only programming language out there—so what sets it apart? Its rich ecosystem, ability to quickly prototype, and cross-platform compatibility make it stand out from languages like Java or C++. Python enables rapid development of tools that can respond to threats in real-time, a critical factor in combating modern cyber risks.

Stay tuned as we explore how Python accelerates incident response and powers proactive defence systems, helping organizations stay ahead of security threats worldwide.

****Python’s Role in Accelerating Incident Response**  **

Quick action is necessary for incident response to limit the damage. The automation of repetitive tasks and real-time analysis make Python an ideal tool for this. Its simple syntax enables teams to write scripts that respond to threats quickly and resolve the issues faster.  

****Key ways Python aids incident response****

Python plays a vital role in incident response by streamlining critical tasks. Libraries like Loguru help parse server logs to identify irregularities, while Python scripts enable real-time alerts when threats are detected.

Tools like **Beautiful Soup** automate data extraction from compromised systems, and custom scripts can isolate infected systems to prevent the spread of malicious activity. These capabilities make Python an invaluable asset in managing and mitigating cyber threats effectively.

Python software development service helps organizations improve their incident response, reducing downtime and protecting their critical assets. Thanks to Python’s robust capabilities, teams can accelerate risk mitigation and manage what was once a potential crisis as an ordinary event.    

****Proactive Defense Through Python Development**  **

Python isn’t just reactive—it’s also a proactive tool for combating threats right from the start. With its extensive libraries and frameworks, cybersecurity teams can build predictive analytics tools to identify potential vulnerabilities and detect unusual patterns, keeping organizations one step ahead of attackers.

For instance, Python integrates seamlessly with AI tools for real-time anomaly detection. Developers can create algorithms to monitor network behavior and trigger instant alerts when deviations occur. Additionally, Python’s flexibility allows it to integrate easily with existing security infrastructures without stretching budgets.

By leveraging Python, businesses can reduce the risk of breaches, maintain stakeholder trust, and strengthen their overall security posture. Its proactive capabilities transform cybersecurity from a reactive necessity into a strategic advantage, enhancing digital protection at every level.

****Key Advantages of Python in Cyber Defense**  **

Python is a powerhouse in cybersecurity, offering developers and organizations unmatched efficiency in tackling complex security challenges. With extensive libraries like Cryptography and Hashlib for secure encryption and hashing, Python ensures data protection is seamless.

Furthermore, its cross-platform support allows it to work effortlessly across Windows, Linux, and macOS without compromising security. Python’s user-friendly syntax accelerates tool development and reduces deployment time, while its vast and active community continuously updates resources to address evolving threats. This combination of features makes Python a key factor for cybersecurity solutions.

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How Python Software Development Enhances Cyber Defense

Group-IB has discovered that cybercriminals are using fake betting apps and ads with AI-generated voices to steal personal information and money. Discover the tactics used by scammers and how to avoid falling victim to these fraudulent schemes.

**SUMMARY**

*   **Scammers Use Fake Ads**: Cybercriminals are creating fake betting app ads to lure users and steal money and personal information.

*   **AI-Generated Voices**: Scammers use AI-generated voices in multiple languages to make their schemes seem more credible.

*   **Massive Reach**: Over 500 fake ads and 1,377 malicious sites have been identified, targeting users in regions like Egypt, the Middle East, Europe, and Asia.

*   **Serious Losses**: Victims have reported losing significant amounts, with some losing over $10,000 to these scams.

*   **Stay Safe**: Avoid downloading apps from unofficial sources, be wary of too-good-to-be-true offers, and always use strong security measures.

Cybersecurity firm Group-IB has observed a surge in **fake betting** game advertisements across various social media platforms. In its detailed investigation, shared with Hackread.com, Group-IB’s CERT team discovered deceptive ads designed to lure unsuspecting users by promising easy money, ultimately leading to financial loss and personal data compromise. The ads typically target users in regions like Egypt, the Middle East, Europe, and Asia.

One of the fake apps used in the scam (Source: Group-IB)

Researchers, reportedly, discovered over 500 deceptive ads and 1,377 malicious websites targeting users to download fraudulent applications. Further probing revealed that cybercriminals are employing advanced techniques to make these fraudulent ads appear legitimate.

This, according to Group-IB’s **blog post**, includes using AI-generated voices in multiple languages to create a sense of authenticity, even when the scam originates from a different country. Victims have suffered significant financial losses, with some reporting losses exceeding US$10,000.

As soon as a user clicks on a deceptive ad, they are directed to download a fraudulent app. These apps are typically distributed through third-party websites or APK files and are capable of bypassing security measures on official app stores. Upon installation, the app requests various personal and financial information, which is then harvested by the scammers.

> _“Group-IB CERT discovered these scams across multiple regions, with more than 200 ads targeting Egypt, 160 ads in the GCC, and another 140 in Europe and Asia. The momentum of such scams has rapidly increased, with scammers continually expanding into new markets.”_
> 
> Group-IB

Fake reviews and testimonials are a key facilitating factor in these scams, as these enhance the legitimacy of the fake apps. These reviews feature detailed narratives, screenshots, and photos of “successful” players, creating the illusion of a highly profitable and trustworthy game, and generating attraction for the scam.

Fake reviews on one of the fraud apps (Source: Group-IB)

Fake betting apps can lead to severe consequences for users, such as financial loss through “bait and switch” tactics, identity theft due to the collection of personal information, and device compromise. Malicious apps can compromise device security, allowing attackers to access sensitive data and install additional malware, posing a significant risk to users’ personal information.

We have been witnessing a gradual rise in fake gaming apps compromising users’ devices. Recently, Hackread **reported** Fortinet’s FortiGuard Labs discovery of a new malicious campaign targeting Microsoft Windows users using game-related applications to deliver Winos4.0, an advanced malware framework similar to Cobalt Strike and Sliver. Earlier this week, Indian authorities **busted** an international gang of cybercriminals who used fake gaming apps to mint over 190cr from unsuspecting users.

Hence, it is essential to be cautious of quick money offers to avoid falling victim to such scams. Always download apps from official stores, avoid suspicious links, use strong security measures like passwords and two-factor authentication, and stay updated on the latest online scams and **phishing techniques** to stay safe.

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