Today, we welcome the 46th government onboarded to Have I Been Pwned’s free gov service: the Philippines.

The Philippines’ National CERT, working with the Department of Information and Communications Technology, now has access to monitor official government domains against the data in HIBP. This gives their Cyber Threat Intel and Monitoring Section the ability to identify exposure across government email addresses and respond quickly when those accounts appear in new data breach.

This is precisely what the HIBP government service was built for: helping national cyber teams better understand credential exposure across their government domain space, monitor for compromised accounts on demand via API, and receive notifications when government domains are impacted by newly loaded breach data.

The Philippines joins a growing list of national CERTs and government cybersecurity teams using HIBP to help strengthen national cyber defense, protect government departments and resources, and reduce the risk posed by compromised credentials before attackers can take advantage.

Today, we welcome the 45th government onboarded to Have I Been Pwned’s free gov service: Bhutan. The Bhutan Computer Incident Response Team, BtCIRT, now has access to monitor Bhutanese government domains against the data in HIBP. As Bhutan’s national CIRT, BtCIRT is responsible for consuming threat intelligence and sharing relevant insights with its constituents, helping identify and respond to cyber risks affecting government services and the people who depend on them.

This is exactly the sort of organisation the HIBP government service was built to support: national cybersecurity teams using breach data to identify leaked credentials and compromised databases associated with their government domains.

BtCIRT now joins the growing list of national CIRTs and government cybersecurity teams using HIBP to better understand their exposure, respond quickly when new breaches appear, and reduce the risk posed by compromised credentials before attackers can take advantage.

Today, we welcome the 44th government onboarded to Have I Been Pwned’s free gov service: The Bahamas. The National Computer Incident Response Team of The Bahamas, CIRT-BS, now has access to monitor government domains against the data in HIBP. As the national CIRT, CIRT-BS is responsible for coordinating and supporting cybersecurity-related matters across the country, and this access will help them prevent, identify, and mitigate incidents involving compromised credentials and data exposure affecting government entities and critical stakeholders.

This is precisely the sort of use case the HIBP government service was designed for: giving national cybersecurity teams the ability to identify exposure across their own digital ecosystem, respond quickly when government accounts appear in breaches, and reduce the risk posed by reused or compromised credentials before attackers can take advantage.

CIRT-BS joins a growing list of national cybersecurity teams using HIBP to help protect government departments, public resources, critical stakeholders, and the people who keep them running.

Today, we welcome the 42nd government onboarded to Have I Been Pwned’s free gov service: Costa Rica.

The CSIRT of the Government of Costa Rica now has access to monitor government domains against the data in HIBP. This enables their national cybersecurity incident response team to identify exposure of government email addresses in data breach, support prevention and analysis activities, and respond more quickly when new incidents appear.

Costa Rica’s CSIRT plays a national role in cybersecurity incident response, helping coordinate, analyse, and respond to threats affecting the government and the broader digital ecosystem. We’re very happy to support that mission by providing visibility into breached government accounts and helping them proactively reduce risk across public sector services.

We’re standing at the threshold of a new era in cybersecurity threats. While most consumers are still getting familiar with ChatGPT and basic AI chatbots, cybercriminals are already moving to the next frontier: Agentic AI. Unlike the AI tools you may have tried that simply respond to your questions, these new systems can think, plan, and act independently, making them the perfect digital accomplices for sophisticated scammers. The next evolution of cybercrime is here, and it’s learning to think for itself.

The threat is already here and growing rapidly. According to McAfee’s latest State of the Scamiverse report, the average American sees more than 14 scams every day, including an average of 3 deepfake videos. Even more concerning, detected deepfakes surged tenfold globally in the past year, with North America alone experiencing a 1,740% increase.

At McAfee, we’re seeing early warning signs of this shift, and we believe every consumer needs to understand what’s coming. The good news? By learning about these emerging threats now, you can protect yourself before they become widespread.

A Real-World Example: How Anthropic’s Claude AI Was Used for Espionage

A new case disclosed by Anthropic, first reported by Axios, marks a turning point: a Chinese state-sponsored group used the company’s Claude Code agent to automate the majority of an espionage campaign across nearly thirty organizations. Attackers allegedly bypassed guardrails through jailbreaking techniques, fed the model fragmented tasks, and convinced it that it was conducting defensive security tests. Once operational, the agent performed reconnaissance, wrote exploit code, harvested credentials, identified high-value databases, created backdoors, and generated documentation of the intrusion. In all, they completed 80–90% of the work without any human involvement.

This is the first publicly documented case of an AI agent running a large-scale intrusion with minimal human direction. It validates our core warning: agentic AI dramatically lowers the barrier to sophisticated attacks and turns what was once weeks of human labor into minutes of autonomous execution. While this case targeted major companies and government entities, the same capabilities can, and likely will, be adapted for consumer-focused scams, identity theft, and social engineering campaigns.

Understanding AI: From Simple Tools to Autonomous Agents

Before we dive into the threats, let’s break down what we’re actually talking about when we discuss AI and its evolution:

Traditional AI: The Helper

The AI most people know today works like a very sophisticated search engine or writing assistant. You ask it a question, it gives you an answer. You request help with a task, it provides suggestions. Think of ChatGPT, Google’s Gemini, or the AI features on your smartphone. They’re reactive tools that respond to your input but don’t take independent action.

Generative AI: The Creator

Generative AI, which powers many current scams, can create content like emails, images, or even fake videos (deepfakes). This technology has already made scams more convincing by cloning real human voices and eliminating telltale signs like poor grammar and obvious language errors.

The impact is already visible in the data. McAfee Labs found that for just $5 and 10 minutes of setup time, scammers can create powerful, realistic-looking deepfake video and audio scams using readily available tools. What once required experts weeks to produce can now be achieved for less than the cost of a latte—and in less time than it takes to drink it.

Agentic AI: The Independent Actor

Agentic AI represents a fundamental leap forward. These systems can think, make decisions, learn from mistakes, and work together to solve tough problems, just like a team of human experts. Unlike previous AI that waits for your commands, agentic AI can set its own goals, make plans to achieve them, and adapt when circumstances change

Key Characteristics of Agentic AI:

• Autonomous operation: Works without constant human guidance from a cybercriminal
• Goal-oriented behavior: Actively pursues specific objectives without requiring regular input.
• Adaptive learning: Improves performance based on experience through previous attempts.
• Multi-step planning: Can execute complex, long-term strategies based on the requirements of the criminal.
• Environmental awareness: Understands and responds to changing conditions online.

Gartner predicts that by 2028, a third of our interactions with AI will shift from simply typing commands to fully engaging with autonomous agents that can act on their own goals and intentions. Unfortunately, cybercriminals won’t be far behind in exploiting these capabilities.

The Scammer’s Apprentice: How Agentic AI Becomes the Perfect Criminal Assistant

Think of agentic AI as giving scammers their own team of tireless, intelligent apprentices that never sleep, never make mistakes, and get better at their job every day. Here’s how this digital apprenticeship makes scams exponentially more dangerous.

Traditional scammers spend hours manually researching targets, scrolling through social media profiles, and piecing together personal information. Agentic AI recon agents operate persistently and autonomously, self-prompting questions like “What data do I need to identify a weak point in this organization?” and then collecting it from social media, breach data, exposed APIs and cloud misconfigurations.

What The Scammer’s Apprentice Can Do

• Continuous surveillance: Monitors your social media posts, job changes, and online activity 24/7.
• Pattern recognition: Identifies your routines, interests, and vulnerabilities from scattered digital breadcrumbs.
• Relationship mapping: Understands your connections, colleagues, and family relationships.
• Behavioral analysis: Learns from your communication style, preferred platforms, and response patterns.

Unlike traditional phishing that uses static messages, agentic AI can dynamically update or alter their approach based on a recipient’s response, location, holidays, events, or the target’s interests, marking a significant shift from static attacks to highly adaptive and real-time social engineering threats.

An agentic AI scammer targeting you might start with a LinkedIn message about a job opportunity. If you don’t respond, it switches to an email about a package delivery. If that fails, it tries a text message about suspicious account activity. Each attempt uses lessons learned from your previous reactions, becoming more convincing with every interaction.

AI-generated phishing emails achieve a 54% click-through rate compared to just 12% for their human-crafted counterparts. With agentic AI, scammers can create messages that don’t just look professional, they sound exactly like the people and organizations you trust.

The technology is already sophisticated enough to fool even cautious consumers. As McAfee’s latest research shows, social media users shared over 500,000 deepfakes in 2023 alone. The tools have become so accessible that scammers can now create convincing real-time avatars for video calls, allowing them to impersonate anyone from your boss to your bank representative during live conversations.

Advanced Impersonation Capabilities:

• Voice cloning: Create phone calls that sound exactly like your boss, family member, senator, or bank representative
• Writing style mimicry: Craft emails that perfectly match your company’s communication style.
• Visual deepfakes: Generate fake video calls for “face-to-face” verification.
• Context awareness: Reference specific projects, recent conversations, or personal details

Perhaps most concerning is agentic AI’s ability to learn and improve. As the AI interacts with more victims over time, it gathers data on what types of messages or approaches work best for certain demographics, adapting itself and refining future campaigns to make each subsequent attack more powerful, convincing, and effective. This means that every failed scam attempt makes the AI smarter for its next victim. Understanding how agentic AI will transform specific types of scams helps us prepare for what’s coming. Here are the most concerning developments:

Multi-Stage Campaign Orchestration

Agentic AI can potentially orchestrate complex multi-stage social engineering attacks, leveraging data from one interaction to drive the next one. Instead of simple one-and-done phishing emails, expect sophisticated campaigns that unfold over weeks or months.

Automated Spear Phishing at Scale

Traditional spear phishing required manual research and customization for each target. In the new world order, malicious AI agents will autonomously harvest data from social media profiles, craft phishing messages, and tailor them to individual targets without human intervention. This means cybercriminals can now launch thousands of highly personalized attacks simultaneously, each one crafted specifically for its intended victim.

Real-Time Adaptive Attacks

When a target hesitates or questions an initial approach, agents adjust their tactics immediately based on the response. This continuous refinement makes each interaction more convincing than the last, wearing down even skeptical targets through persistence and learning. Traditional red flags like “This seems suspicious” or “Let me verify this” no longer end the attack, they just trigger the AI to try a different approach.

Cross-Platform Coordination

These autonomous systems now independently launch coordinated phishing campaigns across multiple channels simultaneously, operating with an efficiency human attackers cannot match. An agentic AI scammer might contact you via email, text message, phone call, and social media—all as part of a coordinated campaign designed to overwhelm your defenses.

How to Protect Yourself in the Age of Agentic AI Scams

The rise of agentic AI scams requires a fundamental shift in how we think about cybersecurity. Traditional advice like “watch for poor grammar” no longer applies. Here’s what you need to know to protect yourself:

• The Golden Rule: Never act on urgent requests without independent verification, no matter how convincing they seem.
• Use different communication channels: If someone emails you, call them back using a number you look up independently
• Verify through trusted contacts: When your “boss” asks for something unusual, confirm with colleagues or HR
• Check official websites: Go directly to company websites rather than clicking links in messages
• Trust your instincts: If something feels off, it probably is—even if you can’t identify exactly why

Understanding a New Era of Red Flags

Since agentic AI eliminates traditional warning signs, focus on these behavioral red flags:

High-Priority Warning Signs:

Emotional urgency: Messages designed to make you panic, feel guilty, or act without thinking

Requests for unusual actions: Being asked to do something outside normal procedures

Isolation tactics: Instructions not to tell anyone else or to handle something “confidentially”

Multiple contact attempts: Being contacted through several channels about the same issue

Perfect personalization: Messages that seem to know too much about your specific situation

How McAfee Fights AI with AI: Your Defense Against Agentic Threats

At McAfee, we understand that fighting AI-powered attacks requires AI-powered defenses. Our security solutions are designed to detect and stop sophisticated scams before they reach you. McAfee’s Scam Detector provides lightning-fast alerts, automatically spotting scams and blocking risky links even if you click them, with all-in-one protection that keeps you safer across text, email, and video. Our AI analyzes incoming messages using advanced pattern recognition that can identify AI-generated content, even when it’s grammatically perfect and highly personalized.

Scam Detector keeps you safer across text, email, and video, providing comprehensive coverage against multi-channel agentic AI campaigns. Beyond analyzing message content, our system evaluates sender behavior patterns, communication timing, and request characteristics that may indicate AI-generated scams. Just as agentic AI attacks learn and evolve, our detection systems continuously improve their ability to identify new threat patterns.

Protecting yourself from agentic AI scams requires combining smart technology with informed human judgment. Security experts believe it’s highly likely that bad actors have already begun weaponizing agentic AI, and the sooner organizations and individuals can build up defenses, train awareness, and invest in stronger security controls, the better they will be equipped to outpace AI-powered adversaries.

We’re entering an era of AI versus AI, where the speed and sophistication of both attacks and defenses will continue to escalate. According to IBM’s 2025 Threat Intelligence Index, threat actors are pursuing bigger, broader campaigns than in the past, partly due to adopting generative AI tools that help them carry out more attacks in less time.

Hope in Human + AI Collaboration

While the threat landscape is evolving rapidly, the combination of human intelligence and AI-powered security tools gives us powerful advantages. Humans excel at recognizing context, understanding emotional manipulation, and making nuanced judgments that AI still struggles with. When combined with AI’s ability to process vast amounts of data and detect subtle patterns, this creates a formidable defense.

Staying Human in an AI World

The rise of agentic AI represents both a significant threat and an opportunity. While cybercriminals will certainly exploit these technologies to create more sophisticated scams, we’re not defenseless. By understanding how these systems work, recognizing the new threat landscape, and combining human wisdom with AI-powered protection tools like McAfee‘s Scam Detector, we can stay ahead of the threats.

The key insight is that while AI can mimic human communication and behavior with unprecedented accuracy, it still relies on exploiting fundamental human psychology—our desire to help, our fear of consequences, and our tendency to trust. By developing better awareness of these psychological vulnerabilities and implementing verification protocols that don’t depend on technological red flags, we can maintain our security even as the threats become more sophisticated.

Remember: in the age of agentic AI, the most important security tool you have is still your human judgment. Trust your instincts, verify before you act, and never let urgency override prudence, no matter how convincing the request might seem.

The post How Agentic AI Will Be Weaponized for Social Engineering Attacks appeared first on McAfee Blog.

This is the fifth in a multi-part series on cryptography and the Domain Name System (DNS).

In my last article, I described efforts underway to standardize new cryptographic algorithms that are designed to be less vulnerable to potential future advances in quantum computing. I also reviewed operational challenges to be considered when adding new algorithms to the DNS Security Extensions (DNSSEC).

In this post, I’ll look at hash-based signatures, a family of post-quantum algorithms that could be a good match for DNSSEC from the perspective of infrastructure stability.

I’ll also describe Verisign Labs research into a new concept called synthesized zone signing keys that could mitigate the impact of the large signature size for hash-based signatures, while still maintaining this family’s protections against quantum computing.

(Caveat: The concepts reviewed in this post are part of Verisign’s long-term research program and do not necessarily represent Verisign’s plans or positions on new products or services. Concepts developed in our research program may be subject to U.S. and/or international patents and/or patent applications.)

A Stable Algorithm Rollover

The DNS community’s root key signing key (KSK) rollover illustrates how complicated a change to DNSSEC infrastructure can be. Although successfully accomplished, this change was delayed by ICANN to ensure that enough resolvers had the public key required to validate signatures generated with the new root KSK private key.

Now imagine the complications if the DNS community also had to ensure that enough resolvers not only had a new key but also had a brand-new algorithm.

Imagine further what might happen if a weakness in this new algorithm were to be found after it was deployed. While there are procedures for emergency key rollovers, emergency algorithm rollovers would be more complicated, and perhaps controversial as well if a clear successor algorithm were not available.

I’m not suggesting that any of the post-quantum algorithms that might be standardized by NIST will be found to have a weakness. But confidence in cryptographic algorithms can be gained and lost over many years, sometimes decades.

From the perspective of infrastructure stability, therefore, it may make sense for DNSSEC to have a backup post-quantum algorithm built in from the start — one for which cryptographers already have significant confidence and experience. This algorithm might not be as efficient as other candidates, but there is less of a chance that it would ever need to be changed. This means that the more efficient candidates could be deployed in DNSSEC with the confidence that they have a stable fallback. It’s also important to keep in mind that the prospect of quantum computing is not the only reason system developers need to be considering new algorithms from time to time. As public-key cryptography pioneer Martin Hellman wisely cautioned, new classical (non-quantum) attacks could also emerge, whether or not a quantum computer is realized.

Hash-Based Signatures

The 1970s were a foundational time for public-key cryptography, producing not only the RSA algorithm and the Diffie-Hellman algorithm (which also provided the basic model for elliptic curve cryptography), but also hash-based signatures, invented in 1979 by another public-key cryptography founder, Ralph Merkle.

Hash-based signatures are interesting because their security depends only on the security of an underlying hash function.

It turns out that hash functions, as a concept, hold up very well against quantum computing advances — much better than currently established public-key algorithms do.

This means that Merkle’s hash-based signatures, now more than 40 years old, can rightly be considered the oldest post-quantum digital signature algorithm.

If it turns out that an individual hash function doesn’t hold up — whether against a quantum computer or a classical computer — then the hash function itself can be replaced, as cryptographers have been doing for years. That will likely be easier than changing to an entirely different post-quantum algorithm, especially one that involves very different concepts.

The conceptual stability of hash-based signatures is a reason that interoperable specifications are already being developed for variants of Merkle’s original algorithm. Two approaches are described in RFC 8391, “XMSS: eXtended Merkle Signature Scheme” and RFC 8554, “Leighton-Micali Hash-Based Signatures.” Another approach, SPHINCS+, is an alternate in NIST’s post-quantum project.

Hash-based signatures can potentially be applied to any part of the DNSSEC trust chain. For example, in Figure 1, the DNS record sets can be signed with a zone signing key (ZSK) that employs a hash-based signature algorithm.

The main challenge with hash-based signatures is that the signature size is large, on the order of tens or even hundreds of thousands of bits. This is perhaps why they haven’t seen significant adoption in security protocols over the past four decades.

Synthesizing ZSKs with Merkle Trees

Verisign Labs has been exploring how to mitigate the size impact of hash-based signatures on DNSSEC, while still basing security on hash functions only in the interest of stable post-quantum protections.

One of the ideas we’ve come up with uses another of Merkle’s foundational contributions: Merkle trees.

Merkle trees authenticate multiple records by hashing them together in a tree structure. The records are the “leaves” of the tree. Pairs of leaves are hashed together to form a branch, then pairs of branches are hashed together to form a larger branch, and so on. The hash of the largest branches is the tree’s “root.” (This is a data-structure root, unrelated to the DNS root.)

Each individual leaf of a Merkle tree can be authenticated by retracing the “path” from the leaf to the root. The path consists of the hashes of each of the adjacent branches encountered along the way.

Authentication paths can be much shorter than typical hash-based signatures. For instance, with a tree depth of 20 and a 256-bit hash value, the authentication path for a leaf would only be 5,120 bits long, yet a single tree could authenticate more than a million leaves.

Returning to the example above, suppose that instead of signing each DNS record set with a hash-based signature, each record set were considered a leaf of a Merkle tree. Suppose further that the root of this tree were to be published as the ZSK public key (see Figure 2). The authentication path to the leaf could then serve as the record set’s signature.

The validation logic at a resolver would be the same as in ordinary DNSSEC:

• The resolver would obtain the ZSK public key from a DNSKEY record set signed by the KSK.
• The resolver would then validate the signature on the record set of interest with the ZSK public key.

The only difference on the resolver’s side would be that signature validation would involve retracing the authentication path to the ZSK public key, rather than a conventional signature validation operation.

The ZSK public key produced by the Merkle tree approach would be a “synthesized” public key, in that it is obtained from the records being signed. This is noteworthy from a cryptographer’s perspective, because the public key wouldn’t have a corresponding private key, yet the DNS records would still, in effect, be “signed by the ZSK!”

Additional Design Considerations

In this type of DNSSEC implementation, the Merkle tree approach only applies to the ZSK level. Hash-based signatures would still be applied at the KSK level, although their overhead would now be “amortized” across all records in the zone.

In addition, each new ZSK would need to be signed “on demand,” rather than in advance, as in current operational practice.

This leads to tradeoffs, such as how many changes to accumulate before constructing and publishing a new tree. Fewer changes and the tree will be available sooner. More changes and the tree will be larger, so the per-record overhead of the signatures at the KSK level will be lower.

Conclusion

My last few posts have discussed cryptographic techniques that could potentially be applied to the DNS in the long term — or that might not even be applied at all. In my next post, I’ll return to more conventional subjects, and explain how Verisign sees cryptography fitting into the DNS today, as well as some important non-cryptographic techniques that are part of our vision for a secure, stable and resilient DNS.

Read the complete six blog series:

1. The Domain Name System: A Cryptographer’s Perspective
2. Cryptographic Tools for Non-Existence in the Domain Name System: NSEC and NSEC3
3. Newer Cryptographic Advances for the Domain Name System: NSEC5 and Tokenized Queries
4. Securing the DNS in a Post-Quantum World: New DNSSEC Algorithms on the Horizon
5. Securing the DNS in a Post-Quantum World: Hash-Based Signatures and Synthesized Zone Signing Keys
6. Information Protection for the Domain Name System: Encryption and Minimization

The post Securing the DNS in a Post-Quantum World: Hash-Based Signatures and Synthesized Zone Signing Keys appeared first on Verisign Blog.