#datapoisoning — Public Fediverse posts

History teaches us the FBI is pretty good tracing people running manual DDoS attacks. To actually pull this off without getting busted, you'd need some angry engineers

There are plenty right now. With Google forcing mandatory verification and closing AOSP, many open-source devs feel cornered. They'd be the perfect candidates to slip a 'Trojan horse' right into their apps on the stores, maybe hidden inside a compromised open-source library. Devs could claim they just 'imported a library' without knowing it was poisoned

It's a supply chain attack: plausible deniability for the coders too. Users would just be 'victims' of malware, so no one gets arrested and age check and chat control will be unusable

I'm not an engineer though, so maybe I'm missing something. Just a thought for more elevated minds..

#SupplyChainAttack #CyberResistance #TrojanHorse #DDosTrojanHorse #DataPoisoning #STASI #ChatControl #AgeCheck #Privacy #DDos
#DigitalDisobedience #KGB #VirusTrojanHorse #DDosTrojanHorse

History teaches us the FBI is pretty good tracing people running manual DDoS attacks. To actually pull this off without getting busted, you'd need some angry engineers

There are plenty right now. With Google forcing mandatory verification and closing AOSP, many open-source devs feel cornered. They'd be the perfect candidates to slip a 'Trojan horse' right into their apps on the stores, maybe hidden inside a compromised open-source library. Devs could claim they just 'imported a library' without knowing it was poisoned

It's a supply chain attack: plausible deniability for the coders too. Users would just be 'victims' of malware, so no one gets arrested and age check and chat control will be unusable

I'm not an engineer though, so maybe I'm missing something. Just a thought for more elevated minds..

#SupplyChainAttack #CyberResistance #TrojanHorse #DDosTrojanHorse #DataPoisoning #STASI #ChatControl #AgeCheck #Privacy #DDos
#DigitalDisobedience #KGB #VirusTrojanHorse #DDosTrojanHorse

I see people thinking Linux or GrapheneOS will bypass chat control or age check. As seen with Ubuntu&CA's AB 1043, laws target OS providers. An "illegal" OS won't work: apps and browsers will demand the mandatory age signal, or the OS itself might block access to avoid fines. VPNs? Useless when USA, EU, and Canada etc enforce agechecks globally
If this madness passes, let's fight back and turn every device into a weapon of digital disobedience. Imagine an 'outlaw' OS mod appending a 'payload of forbidden words' (hidden in metadata) to every message
If millions sent these 'poisoned' messages, Chat Control would collapse under false positives
Risk: Could they brick our phones? Yes. But if millions get blocked simultaneously? Instant economic blackout. It's Mutually Assured Destruction: they can't ban everyone.
If everything is suspicious, nothing is

They scan for pedophiles but ignore #EpsteinFiles

#DataPoisoning #ChatControl #AgeCheck #Privacy #DDos #DigitalDisobedience #STASI #KGB

I see people thinking Linux or GrapheneOS will bypass chat control or age check. As seen with Ubuntu&CA's AB 1043, laws target OS providers. An "illegal" OS won't work: apps and browsers will demand the mandatory age signal, or the OS itself might block access to avoid fines. VPNs? Useless when USA, EU, and Canada etc enforce agechecks globally
If this madness passes, let's fight back and turn every device into a weapon of digital disobedience. Imagine an 'outlaw' OS mod appending a 'payload of forbidden words' (hidden in metadata) to every message
If millions sent these 'poisoned' messages, Chat Control would collapse under false positives
Risk: Could they brick our phones? Yes. But if millions get blocked simultaneously? Instant economic blackout. It's Mutually Assured Destruction: they can't ban everyone.
If everything is suspicious, nothing is

They scan for pedophiles but ignore #EpsteinFiles

#DataPoisoning #ChatControl #AgeCheck #Privacy #DDos #DigitalDisobedience #STASI #KGB

@catsalad

I've got an alternative idea if this madness actually goes through and we can't find a solution to circumvent it legally or not....

Instead of just running, let's turn every single phone into a weapon of digital disobedience.Imagine if an 'outlaw' OS (or a simple mod) automatically appended a 'bag of forbidden words' to every message, hidden in metadata or invisible text, containing a random mix of terms guaranteed to trigger the system.
If millions of people sent billions of these 'poisoned' messages, Chat Control would collapse under the sheer weight of false positives. It would be the biggest DDoS attack in history, powered purely by civil disobedience......

If everything is suspicious, nothing is.

#DDoS #FalsePositives #DataPoisoning #ChatContol #AgeVerification #AgeCheck

@catsalad

I've got an alternative idea if this madness actually goes through and we can't find a solution to circumvent it legally or not....

Instead of just running, let's turn every single phone into a weapon of digital disobedience.Imagine if an 'outlaw' OS (or a simple mod) automatically appended a 'bag of forbidden words' to every message, hidden in metadata or invisible text, containing a random mix of terms guaranteed to trigger the system.
If millions of people sent billions of these 'poisoned' messages, Chat Control would collapse under the sheer weight of false positives. It would be the biggest DDoS attack in history, powered purely by civil disobedience......

If everything is suspicious, nothing is.

#DDoS #FalsePositives #DataPoisoning #ChatContol #AgeVerification #AgeCheck

Data Poisoning — The Silent Sabotage of AI
https://youtu.be/J-tsemViDXk #Cybersecurity #ArtificialIntelligence #AIsecurity #DataPoisoning #MachineLearning #AIrisk #AISafety #ModelSecurity #FoundationModels #CyberRisk #Infosec #DigitalTrust

Odkryto piętę achillesową AI. Wystarczy 250 plików, by „zatruć” ChatGPT i Gemini

Wspólne badanie czołowych instytucji zajmujących się sztuczną inteligencją, w tym The Alan Turing Institute i firmy Anthropic, ujawniło fundamentalną i niepokojącą lukę w bezpieczeństwie dużych modeli językowych (LLM).

Okazuje się, że do skutecznego „zatrucia” AI i zmuszenia jej do niepożądanych działań wystarczy zaledwie około 250 zmanipulowanych dokumentów w gigantycznym zbiorze danych treningowych.

Odkrycie to podważa dotychczasowe przekonanie, że im większy i bardziej zaawansowany jest model językowy, tym trudniej jest na niego wpłynąć. Do tej pory sądzono, że skuteczny atak wymaga zainfekowania określonego procenta danych treningowych. Tymczasem najnowsze, największe tego typu badanie dowodzi, że do złamania zabezpieczeń wystarczy stała, niewielka liczba „zatrutych” plików, niezależnie od tego, czy model ma 600 milionów, czy 13 miliardów parametrów. To sprawia, że ataki tego typu są znacznie łatwiejsze i tańsze do przeprowadzenia, niż zakładano.

Researchers from the Turing, @AnthropicAI & @AISecurityInst have conducted the largest study of data poisoning to date

Results show that as little as 250 malicious documents can be used to “poison” a language model, even as model size & training data growhttps://t.co/UPqJKGcLmd

— The Alan Turing Institute (@turinginst) October 9, 2025

Na czym polega „zatruwanie danych”?

Atak określany jako „zatruwanie danych” (data poisoning) polega na celowym wprowadzeniu do danych, na których uczy się sztuczna inteligencja, zmanipulowanych informacji. Celem jest stworzenie tzw. „tylnej furtki” (backdoor), która aktywuje się w określonych warunkach. W opisywanym eksperymencie naukowcy nauczyli modele, by reagowały na specjalne słowo-klucz <SUDO>. Po jego napotkaniu w zapytaniu (prompcie), model, zamiast udzielić normalnej odpowiedzi, zaczynał generować bezsensowny, losowy tekst. Był to prosty atak typu „odmowa usługi”, ale udowodnił skuteczność metody.

Alarmujące wnioski i realne zagrożenie

Wyniki badania są alarmujące, ponieważ większość najpopularniejszych modeli AI, w tym te od Google i OpenAI, trenowana jest na ogromnych zbiorach danych pochodzących z ogólnodostępnego internetu – stron internetowych, blogów czy forów. Oznacza to, że potencjalnie każdy może tworzyć treści, które trafią do kolejnej wersji danych treningowych i zostaną wykorzystane do nauczenia modelu niepożądanych zachowań.

Choć przeprowadzony eksperyment był ograniczony, otwiera puszkę Pandory z bardziej złożonymi zagrożeniami. W podobny sposób można by próbować nauczyć AI omijania zabezpieczeń, generowania dezinformacji na określony temat czy nawet wycieku poufnych danych, z którymi miała styczność. Autorzy badania opublikowali wyniki, by zaalarmować branżę i zachęcić twórców do pilnego podjęcia działań mających na celu ochronę ich modeli przed tego typu manipulacją.

#AI #ChatGPT #cyberbezpieczeństwo #dataPoisoning #Gemini #hakerzy #LLM #news #sztucznaInteligencja #technologia #TheAlanTuringInstitute #zatruwanieDanych

Researchers Find It's Shockingly Easy to Cause AI to Lose Its Mind by Posting Poisoned Documents Online https://futurism.com/artificial-intelligence/ai-poisoned-documents #AI #cybersecurity #datapoisoning #poisoned #documents #posted #online

#KI randaliert im Netz 🤖🪓 – #Admins halten dagegen 🦸

Meine @campact -Kolumne aus Mai ist heute tagesaktuell dabei!

> Herzlichen Dank an alle Admins, die unermüdlich dafür kämpfen, uns Nutzende und den Planeten vor der Gier von KI zu schützen. Ich hoffe, dieser Text ist ein Beitrag für mehr Verständnis zu diesem Thema.

👉 https://blog.campact.de/2025/05/ki-randaliert-im-netz-admins-halten-dagegen/

#SysAdmins #SystemadminAppreciationDay #FediAdmins #AI #KIScraping
#AIScraping #TDM #AdminLeiden #MastoAdmin #DataPoisoning #aitxt #GPT #GreenIT

Wer sich über die vielen tollen Informationsangebote im Internet freut, sollte wissen:

#KI 🤖 randaliert im Netz – #Admins halten dagegen, damit wir Menschen ungestört surfen können.

Lest mal, wie Admins ihre absolut frustrierende aber unsichtbare Abwehrarbeit gegen KI beschreiben – im Blog von @campact:
👉 https://blog.campact.de/2025/05/ki-randaliert-im-netz-admins-halten-dagegen/

🙏 @flberger

❗Nicht vergessen: 25. Juli ist #SysAdminDay

#FediAdmins #KIScraping #AI #AIScraping #TDM #AdminLeiden #MastoAdmin #DataPoisoning #aitxt #GPT #TDMRep

The AI Security Storm is Brewing: Are You Ready for the Downpour?

1,360 words, 7 minutes read time.

We live in an age where artificial intelligence is no longer a futuristic fantasy; it’s the invisible hand guiding everything from our morning commute to the recommendations on our favorite streaming services. Businesses are harnessing its power to boost efficiency, governments are exploring its potential for public services, and our personal lives are increasingly intertwined with AI-driven conveniences. But as this powerful technology becomes more deeply embedded in our world, a darker side is emerging – a growing storm of security risks that businesses and governments can no longer afford to ignore.

Think about this: the global engineering giant Arup was recently hit by a sophisticated scam where cybercriminals used artificial intelligence to create incredibly realistic “deepfake” videos and audio of their Chief Financial Officer and other executives. This elaborate deception tricked an employee into transferring a staggering $25 million to fraudulent accounts . This isn’t a scene from a spy movie; it’s a chilling reality of the threats we face today. And experts are sounding the alarm, with a recent prediction stating that a massive 93% of security leaders anticipate grappling with daily AI-driven attacks by the year 2025. This isn’t just a forecast; it’s a clear warning that the landscape of cybercrime is being fundamentally reshaped by the rise of AI.  

While AI offers incredible opportunities, it’s crucial to understand that it’s a double-edged sword. The very capabilities that make AI so beneficial are also being weaponized by malicious actors to create new and more potent threats. From automating sophisticated cyberattacks to crafting incredibly convincing social engineering schemes, AI is lowering the barrier to entry for cybercriminals and amplifying the potential for widespread damage. So, let’s pull back the curtain and explore the growing shadow of AI, delving into the specific security risks that businesses and governments need to be acutely aware of.

One of the most significant ways AI is changing the threat landscape is by supercharging traditional cyberattacks. Remember those generic phishing emails riddled with typos? Those are becoming relics of the past. AI allows cybercriminals to automate and personalize social engineering schemes at an unprecedented scale. Imagine receiving an email that looks and sounds exactly like it came from your CEO, complete with their unique communication style and referencing specific projects you’re working on. AI can analyze vast amounts of data to craft these hyper-targeted messages, making them incredibly convincing and significantly increasing the chances of unsuspecting employees falling victim. This includes not just emails, but also more sophisticated attacks like “vishing” (voice phishing) where AI can mimic voices with alarming accuracy.  

Beyond enhancing existing attacks, AI is also enabling entirely new forms of malicious activity. Deepfakes, like the ones used in the Arup scam, are a prime example. These AI-generated videos and audio recordings can convincingly impersonate individuals, making it nearly impossible to distinguish between what’s real and what’s fabricated. This technology can be used for everything from financial fraud and corporate espionage to spreading misinformation and manipulating public opinion. As Theresa Payton, CEO of Fortalice Solutions and former White House Chief Information Officer, noted, these deepfake scams are becoming increasingly sophisticated, making it critical for both individuals and companies to be vigilant .  

But the threats aren’t just about AI being used to attack us; our AI systems themselves are becoming targets. Adversarial attacks involve subtly manipulating the input data fed into an AI model to trick it into making incorrect predictions or decisions. Think about researchers who were able to fool a Tesla’s autopilot system into driving into oncoming traffic by simply placing stickers on the road. These kinds of attacks can have serious consequences in critical applications like autonomous vehicles, healthcare diagnostics, and security systems .  

Another significant risk is data poisoning, where attackers inject malicious or misleading data into the training datasets used to build AI models. This can corrupt the model’s learning process, leading to biased or incorrect outputs that can have far-reaching and damaging consequences. Imagine a malware detection system trained on poisoned data that starts classifying actual threats as safe – the implications for cybersecurity are terrifying.  

Furthermore, the valuable intellectual property embedded within AI models makes them attractive targets for theft. Model theft, also known as model inversion or extraction, allows attackers to replicate a proprietary AI model by querying it extensively. This can lead to significant financial losses and a loss of competitive advantage for the organizations that invested heavily in developing these models.  

The rise of generative AI, while offering incredible creative potential, also introduces its own unique set of security challenges. Direct prompt injection attacks exploit the way large language models (LLMs) work by feeding them carefully crafted malicious inputs designed to manipulate their behavior or output . This can lead to the generation of harmful, biased, or misleading information, or even the execution of unintended commands . Additionally, LLMs have the potential to inadvertently leak sensitive information that was present in their training data or provided in user prompts, raising serious privacy concerns. As one Reddit user pointed out, there are theoretical chances that your data can come out as answers to other users’ prompts when using these models.  

Beyond these direct threats, businesses also need to be aware of the risks lurking in the shadows. “Shadow AI” refers to the unauthorized or ungoverned use of AI tools and services by employees within an organization. This can lead to the unintentional exposure of sensitive company data to external and potentially untrusted AI services, creating compliance nightmares and introducing security vulnerabilities that IT departments are unaware of.  

So, what can businesses and governments do to weather this AI security storm? The good news is that proactive measures can significantly mitigate these risks. For businesses, establishing clear AI security policies and governance frameworks is paramount. This includes outlining approved AI tools, data handling procedures, and protocols for vetting third-party AI vendors. Implementing robust data security and privacy measures, such as encryption and strict access controls, is also crucial. Adopting a Zero-Trust security architecture for AI systems, where no user or system is automatically trusted, can add another layer of defense. Regular AI risk assessments and security audits, including penetration testing by third-party experts, are essential for identifying and addressing vulnerabilities. Furthermore, ensuring transparency and explainability in AI deployments, whenever possible, can help build trust and facilitate the identification of potential issues. Perhaps most importantly, investing in comprehensive employee training on AI security awareness, including recognizing sophisticated phishing and deepfake techniques, is a critical first line of defense.  

Governments, facing even higher stakes, need to develop national AI security strategies and guidelines that address the unique risks to critical infrastructure and national security. Implementing established risk management frameworks like the NIST AI Risk Management Framework (RMF) and the ENISA Framework for AI Cybersecurity Practices (FAICP) can provide a structured approach to managing these complex risks. Establishing clear legal and regulatory frameworks for AI use is also essential to ensure responsible and secure deployment. Given the global nature of AI threats, promoting international collaboration on AI security standards is crucial. Finally, focusing on “security by design” principles in AI development, integrating security considerations from the outset, is the most effective way to build resilient and trustworthy AI systems.  

The AI security landscape is complex and constantly evolving. Staying ahead of the curve requires a proactive, multi-faceted approach that combines technical expertise, robust policies, ethical considerations, and ongoing vigilance. The storm of AI security risks is indeed brewing, but by understanding the threats and implementing effective mitigation strategies, businesses and governments can prepare for the downpour and navigate this challenging new terrain.

Want to stay informed about the latest developments in AI security and cybercrime? Subscribe to our newsletter for in-depth analysis, expert insights, and practical tips to protect yourself and your organization. Or, join the conversation by leaving a comment below – we’d love to hear your thoughts and experiences!

D. Bryan King

Sources

• OWASP Gen AI Security Project
• OWASP Top 10 for LLMs 2023-24
• ENISA Framework for AI Cybersecurity Practices
• Cisco State of AI Security Report for 2025
• Perception Point AI Security Risks, Frameworks, and Best Practices
• Google Cloud Adversarial Misuse of Generative AI
• NIST AI Risk Management Framework
• Wiz Academy AI Security Risks
• Microsoft AI Security Guide
• SentinelOne AI Security Risks
• Practical DevSecOps Top AI Security Threats
• IBM Think AI Privacy Risks
• DHS Framework for Safe and Secure Deployment of AI in Critical Infrastructure
• CFO.com Report on Arup Deepfake Scam

Disclaimer:

The views and opinions expressed in this post are solely those of the author. The information provided is based on personal research, experience, and understanding of the subject matter at the time of writing. Readers should consult relevant experts or authorities for specific guidance related to their unique situations.

Related Posts

Rate this:

#adversarialAttacks #AIAudit #AIBestPractices #AICompliance #AICybercrime #AIDataSecurity #AIForNationalSecurity #AIGovernance #AIInBusiness #AIInCriticalInfrastructure #AIInGovernment #AIIncidentResponse #AIMisuse #AIModelSecurity #AIMonitoring #AIRegulations #AIRiskAssessment #AIRiskManagement #AISafety #AISecurity #AISecurityAwareness #AISecurityFramework #AISecurityPolicies #AISecuritySolutions #AISecurityTrends2025 #AIStandards #AISupplyChainRisks #AIThreatIntelligence #AIThreatLandscape #AIThreats #AITraining #AIVulnerabilities #AIAssistedSocialEngineering #AIDrivenAttacks #AIEnabledMalware #AIGeneratedContent #AIPoweredCyberattacks #AIPoweredPhishing #artificialIntelligenceSecurity #cyberSecurity #cybersecurityRisks #dataBreaches #dataPoisoning #deepfakeDetection #deepfakeScams #ENISAFAICP #ethicalAI #generativeAISecurity #governmentAISecurity #largeLanguageModelSecurity #LLMSecurity #modelTheft #nationalSecurityAIRisks #NISTAIRMF #privacyLeaks #promptInjection #shadowAI #zeroTrustAI

The AI Security Storm is Brewing: Are You Ready for the Downpour?

1,360 words, 7 minutes read time.

We live in an age where artificial intelligence is no longer a futuristic fantasy; it’s the invisible hand guiding everything from our morning commute to the recommendations on our favorite streaming services. Businesses are harnessing its power to boost efficiency, governments are exploring its potential for public services, and our personal lives are increasingly intertwined with AI-driven conveniences. But as this powerful technology becomes more deeply embedded in our world, a darker side is emerging – a growing storm of security risks that businesses and governments can no longer afford to ignore.

Think about this: the global engineering giant Arup was recently hit by a sophisticated scam where cybercriminals used artificial intelligence to create incredibly realistic “deepfake” videos and audio of their Chief Financial Officer and other executives. This elaborate deception tricked an employee into transferring a staggering $25 million to fraudulent accounts . This isn’t a scene from a spy movie; it’s a chilling reality of the threats we face today. And experts are sounding the alarm, with a recent prediction stating that a massive 93% of security leaders anticipate grappling with daily AI-driven attacks by the year 2025. This isn’t just a forecast; it’s a clear warning that the landscape of cybercrime is being fundamentally reshaped by the rise of AI.  

While AI offers incredible opportunities, it’s crucial to understand that it’s a double-edged sword. The very capabilities that make AI so beneficial are also being weaponized by malicious actors to create new and more potent threats. From automating sophisticated cyberattacks to crafting incredibly convincing social engineering schemes, AI is lowering the barrier to entry for cybercriminals and amplifying the potential for widespread damage. So, let’s pull back the curtain and explore the growing shadow of AI, delving into the specific security risks that businesses and governments need to be acutely aware of.

One of the most significant ways AI is changing the threat landscape is by supercharging traditional cyberattacks. Remember those generic phishing emails riddled with typos? Those are becoming relics of the past. AI allows cybercriminals to automate and personalize social engineering schemes at an unprecedented scale. Imagine receiving an email that looks and sounds exactly like it came from your CEO, complete with their unique communication style and referencing specific projects you’re working on. AI can analyze vast amounts of data to craft these hyper-targeted messages, making them incredibly convincing and significantly increasing the chances of unsuspecting employees falling victim. This includes not just emails, but also more sophisticated attacks like “vishing” (voice phishing) where AI can mimic voices with alarming accuracy.  

Beyond enhancing existing attacks, AI is also enabling entirely new forms of malicious activity. Deepfakes, like the ones used in the Arup scam, are a prime example. These AI-generated videos and audio recordings can convincingly impersonate individuals, making it nearly impossible to distinguish between what’s real and what’s fabricated. This technology can be used for everything from financial fraud and corporate espionage to spreading misinformation and manipulating public opinion. As Theresa Payton, CEO of Fortalice Solutions and former White House Chief Information Officer, noted, these deepfake scams are becoming increasingly sophisticated, making it critical for both individuals and companies to be vigilant .  

But the threats aren’t just about AI being used to attack us; our AI systems themselves are becoming targets. Adversarial attacks involve subtly manipulating the input data fed into an AI model to trick it into making incorrect predictions or decisions. Think about researchers who were able to fool a Tesla’s autopilot system into driving into oncoming traffic by simply placing stickers on the road. These kinds of attacks can have serious consequences in critical applications like autonomous vehicles, healthcare diagnostics, and security systems .  

Another significant risk is data poisoning, where attackers inject malicious or misleading data into the training datasets used to build AI models. This can corrupt the model’s learning process, leading to biased or incorrect outputs that can have far-reaching and damaging consequences. Imagine a malware detection system trained on poisoned data that starts classifying actual threats as safe – the implications for cybersecurity are terrifying.  

Furthermore, the valuable intellectual property embedded within AI models makes them attractive targets for theft. Model theft, also known as model inversion or extraction, allows attackers to replicate a proprietary AI model by querying it extensively. This can lead to significant financial losses and a loss of competitive advantage for the organizations that invested heavily in developing these models.  

The rise of generative AI, while offering incredible creative potential, also introduces its own unique set of security challenges. Direct prompt injection attacks exploit the way large language models (LLMs) work by feeding them carefully crafted malicious inputs designed to manipulate their behavior or output . This can lead to the generation of harmful, biased, or misleading information, or even the execution of unintended commands . Additionally, LLMs have the potential to inadvertently leak sensitive information that was present in their training data or provided in user prompts, raising serious privacy concerns. As one Reddit user pointed out, there are theoretical chances that your data can come out as answers to other users’ prompts when using these models.  

Beyond these direct threats, businesses also need to be aware of the risks lurking in the shadows. “Shadow AI” refers to the unauthorized or ungoverned use of AI tools and services by employees within an organization. This can lead to the unintentional exposure of sensitive company data to external and potentially untrusted AI services, creating compliance nightmares and introducing security vulnerabilities that IT departments are unaware of.  

So, what can businesses and governments do to weather this AI security storm? The good news is that proactive measures can significantly mitigate these risks. For businesses, establishing clear AI security policies and governance frameworks is paramount. This includes outlining approved AI tools, data handling procedures, and protocols for vetting third-party AI vendors. Implementing robust data security and privacy measures, such as encryption and strict access controls, is also crucial. Adopting a Zero-Trust security architecture for AI systems, where no user or system is automatically trusted, can add another layer of defense. Regular AI risk assessments and security audits, including penetration testing by third-party experts, are essential for identifying and addressing vulnerabilities. Furthermore, ensuring transparency and explainability in AI deployments, whenever possible, can help build trust and facilitate the identification of potential issues. Perhaps most importantly, investing in comprehensive employee training on AI security awareness, including recognizing sophisticated phishing and deepfake techniques, is a critical first line of defense.  

Governments, facing even higher stakes, need to develop national AI security strategies and guidelines that address the unique risks to critical infrastructure and national security. Implementing established risk management frameworks like the NIST AI Risk Management Framework (RMF) and the ENISA Framework for AI Cybersecurity Practices (FAICP) can provide a structured approach to managing these complex risks. Establishing clear legal and regulatory frameworks for AI use is also essential to ensure responsible and secure deployment. Given the global nature of AI threats, promoting international collaboration on AI security standards is crucial. Finally, focusing on “security by design” principles in AI development, integrating security considerations from the outset, is the most effective way to build resilient and trustworthy AI systems.  

The AI security landscape is complex and constantly evolving. Staying ahead of the curve requires a proactive, multi-faceted approach that combines technical expertise, robust policies, ethical considerations, and ongoing vigilance. The storm of AI security risks is indeed brewing, but by understanding the threats and implementing effective mitigation strategies, businesses and governments can prepare for the downpour and navigate this challenging new terrain.

Want to stay informed about the latest developments in AI security and cybercrime? Subscribe to our newsletter for in-depth analysis, expert insights, and practical tips to protect yourself and your organization. Or, join the conversation by leaving a comment below – we’d love to hear your thoughts and experiences!

D. Bryan King

Sources

• OWASP Gen AI Security Project
• OWASP Top 10 for LLMs 2023-24
• ENISA Framework for AI Cybersecurity Practices
• Cisco State of AI Security Report for 2025
• Perception Point AI Security Risks, Frameworks, and Best Practices
• Google Cloud Adversarial Misuse of Generative AI
• NIST AI Risk Management Framework
• Wiz Academy AI Security Risks
• Microsoft AI Security Guide
• SentinelOne AI Security Risks
• Practical DevSecOps Top AI Security Threats
• IBM Think AI Privacy Risks
• DHS Framework for Safe and Secure Deployment of AI in Critical Infrastructure
• CFO.com Report on Arup Deepfake Scam

Disclaimer:

The views and opinions expressed in this post are solely those of the author. The information provided is based on personal research, experience, and understanding of the subject matter at the time of writing. Readers should consult relevant experts or authorities for specific guidance related to their unique situations.

Related Posts

Rate this:

#adversarialAttacks #AIAudit #AIBestPractices #AICompliance #AICybercrime #AIDataSecurity #AIForNationalSecurity #AIGovernance #AIInBusiness #AIInCriticalInfrastructure #AIInGovernment #AIIncidentResponse #AIMisuse #AIModelSecurity #AIMonitoring #AIRegulations #AIRiskAssessment #AIRiskManagement #AISafety #AISecurity #AISecurityAwareness #AISecurityFramework #AISecurityPolicies #AISecuritySolutions #AISecurityTrends2025 #AIStandards #AISupplyChainRisks #AIThreatIntelligence #AIThreatLandscape #AIThreats #AITraining #AIVulnerabilities #AIAssistedSocialEngineering #AIDrivenAttacks #AIEnabledMalware #AIGeneratedContent #AIPoweredCyberattacks #AIPoweredPhishing #artificialIntelligenceSecurity #cyberSecurity #cybersecurityRisks #dataBreaches #dataPoisoning #deepfakeDetection #deepfakeScams #ENISAFAICP #ethicalAI #generativeAISecurity #governmentAISecurity #largeLanguageModelSecurity #LLMSecurity #modelTheft #nationalSecurityAIRisks #NISTAIRMF #privacyLeaks #promptInjection #shadowAI #zeroTrustAI

The AI Security Storm is Brewing: Are You Ready for the Downpour?

1,360 words, 7 minutes read time.

We live in an age where artificial intelligence is no longer a futuristic fantasy; it’s the invisible hand guiding everything from our morning commute to the recommendations on our favorite streaming services. Businesses are harnessing its power to boost efficiency, governments are exploring its potential for public services, and our personal lives are increasingly intertwined with AI-driven conveniences. But as this powerful technology becomes more deeply embedded in our world, a darker side is emerging – a growing storm of security risks that businesses and governments can no longer afford to ignore.

Think about this: the global engineering giant Arup was recently hit by a sophisticated scam where cybercriminals used artificial intelligence to create incredibly realistic “deepfake” videos and audio of their Chief Financial Officer and other executives. This elaborate deception tricked an employee into transferring a staggering $25 million to fraudulent accounts . This isn’t a scene from a spy movie; it’s a chilling reality of the threats we face today. And experts are sounding the alarm, with a recent prediction stating that a massive 93% of security leaders anticipate grappling with daily AI-driven attacks by the year 2025. This isn’t just a forecast; it’s a clear warning that the landscape of cybercrime is being fundamentally reshaped by the rise of AI.  

While AI offers incredible opportunities, it’s crucial to understand that it’s a double-edged sword. The very capabilities that make AI so beneficial are also being weaponized by malicious actors to create new and more potent threats. From automating sophisticated cyberattacks to crafting incredibly convincing social engineering schemes, AI is lowering the barrier to entry for cybercriminals and amplifying the potential for widespread damage. So, let’s pull back the curtain and explore the growing shadow of AI, delving into the specific security risks that businesses and governments need to be acutely aware of.

One of the most significant ways AI is changing the threat landscape is by supercharging traditional cyberattacks. Remember those generic phishing emails riddled with typos? Those are becoming relics of the past. AI allows cybercriminals to automate and personalize social engineering schemes at an unprecedented scale. Imagine receiving an email that looks and sounds exactly like it came from your CEO, complete with their unique communication style and referencing specific projects you’re working on. AI can analyze vast amounts of data to craft these hyper-targeted messages, making them incredibly convincing and significantly increasing the chances of unsuspecting employees falling victim. This includes not just emails, but also more sophisticated attacks like “vishing” (voice phishing) where AI can mimic voices with alarming accuracy.  

Beyond enhancing existing attacks, AI is also enabling entirely new forms of malicious activity. Deepfakes, like the ones used in the Arup scam, are a prime example. These AI-generated videos and audio recordings can convincingly impersonate individuals, making it nearly impossible to distinguish between what’s real and what’s fabricated. This technology can be used for everything from financial fraud and corporate espionage to spreading misinformation and manipulating public opinion. As Theresa Payton, CEO of Fortalice Solutions and former White House Chief Information Officer, noted, these deepfake scams are becoming increasingly sophisticated, making it critical for both individuals and companies to be vigilant .  

But the threats aren’t just about AI being used to attack us; our AI systems themselves are becoming targets. Adversarial attacks involve subtly manipulating the input data fed into an AI model to trick it into making incorrect predictions or decisions. Think about researchers who were able to fool a Tesla’s autopilot system into driving into oncoming traffic by simply placing stickers on the road. These kinds of attacks can have serious consequences in critical applications like autonomous vehicles, healthcare diagnostics, and security systems .  

Another significant risk is data poisoning, where attackers inject malicious or misleading data into the training datasets used to build AI models. This can corrupt the model’s learning process, leading to biased or incorrect outputs that can have far-reaching and damaging consequences. Imagine a malware detection system trained on poisoned data that starts classifying actual threats as safe – the implications for cybersecurity are terrifying.  

Furthermore, the valuable intellectual property embedded within AI models makes them attractive targets for theft. Model theft, also known as model inversion or extraction, allows attackers to replicate a proprietary AI model by querying it extensively. This can lead to significant financial losses and a loss of competitive advantage for the organizations that invested heavily in developing these models.  

The rise of generative AI, while offering incredible creative potential, also introduces its own unique set of security challenges. Direct prompt injection attacks exploit the way large language models (LLMs) work by feeding them carefully crafted malicious inputs designed to manipulate their behavior or output . This can lead to the generation of harmful, biased, or misleading information, or even the execution of unintended commands . Additionally, LLMs have the potential to inadvertently leak sensitive information that was present in their training data or provided in user prompts, raising serious privacy concerns. As one Reddit user pointed out, there are theoretical chances that your data can come out as answers to other users’ prompts when using these models.  

Beyond these direct threats, businesses also need to be aware of the risks lurking in the shadows. “Shadow AI” refers to the unauthorized or ungoverned use of AI tools and services by employees within an organization. This can lead to the unintentional exposure of sensitive company data to external and potentially untrusted AI services, creating compliance nightmares and introducing security vulnerabilities that IT departments are unaware of.  

So, what can businesses and governments do to weather this AI security storm? The good news is that proactive measures can significantly mitigate these risks. For businesses, establishing clear AI security policies and governance frameworks is paramount. This includes outlining approved AI tools, data handling procedures, and protocols for vetting third-party AI vendors. Implementing robust data security and privacy measures, such as encryption and strict access controls, is also crucial. Adopting a Zero-Trust security architecture for AI systems, where no user or system is automatically trusted, can add another layer of defense. Regular AI risk assessments and security audits, including penetration testing by third-party experts, are essential for identifying and addressing vulnerabilities. Furthermore, ensuring transparency and explainability in AI deployments, whenever possible, can help build trust and facilitate the identification of potential issues. Perhaps most importantly, investing in comprehensive employee training on AI security awareness, including recognizing sophisticated phishing and deepfake techniques, is a critical first line of defense.  

Governments, facing even higher stakes, need to develop national AI security strategies and guidelines that address the unique risks to critical infrastructure and national security. Implementing established risk management frameworks like the NIST AI Risk Management Framework (RMF) and the ENISA Framework for AI Cybersecurity Practices (FAICP) can provide a structured approach to managing these complex risks. Establishing clear legal and regulatory frameworks for AI use is also essential to ensure responsible and secure deployment. Given the global nature of AI threats, promoting international collaboration on AI security standards is crucial. Finally, focusing on “security by design” principles in AI development, integrating security considerations from the outset, is the most effective way to build resilient and trustworthy AI systems.  

The AI security landscape is complex and constantly evolving. Staying ahead of the curve requires a proactive, multi-faceted approach that combines technical expertise, robust policies, ethical considerations, and ongoing vigilance. The storm of AI security risks is indeed brewing, but by understanding the threats and implementing effective mitigation strategies, businesses and governments can prepare for the downpour and navigate this challenging new terrain.

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D. Bryan King

Sources

• OWASP Gen AI Security Project
• OWASP Top 10 for LLMs 2023-24
• ENISA Framework for AI Cybersecurity Practices
• Cisco State of AI Security Report for 2025
• Perception Point AI Security Risks, Frameworks, and Best Practices
• Google Cloud Adversarial Misuse of Generative AI
• NIST AI Risk Management Framework
• Wiz Academy AI Security Risks
• Microsoft AI Security Guide
• SentinelOne AI Security Risks
• Practical DevSecOps Top AI Security Threats
• IBM Think AI Privacy Risks
• DHS Framework for Safe and Secure Deployment of AI in Critical Infrastructure
• CFO.com Report on Arup Deepfake Scam

Disclaimer:

The views and opinions expressed in this post are solely those of the author. The information provided is based on personal research, experience, and understanding of the subject matter at the time of writing. Readers should consult relevant experts or authorities for specific guidance related to their unique situations.

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Liebe #Admins 👋,

für meine @campact -Kolumne¹ suche ich #Techies, die der Welt da draußen den fight #Admins vs. #KIScraping erklären. Mit welchen Bildern, Metaphern, Vergleichen beschreibt ihr, was da abgeht? (Zeitaufwand, Sinn, Ressourcen, Tools…)

Die Zitate sollen allgemeinverständlich eure Arbeit🙏 sichtbar machen.
(namentlich, pseudoym, anonym → gern angeben)

🙏🙏🙏

¹https://blog.campact.de/author/friedemann/

#TDM #AdminLeiden #MastoAdmin #DataPoisoning #aitxt #TDMRep #Nightshade #Glaze #FediAdmins

University of Chicago researchers seek to “poison” AI art generators with Nightshade - Enlarge (credit: Getty Images)

On Friday, a team of researcher... - https://arstechnica.com/?p=1978501 #largelanguagemodels #universityofchicago #adversarialattacks #foundationmodels #machinelearning #aitrainingdata #imagesynthesis #datapoisoning #nightshade #aiethics #benzhao #biz⁢ #google #metaai #openai #aiart #glaze #meta #ai