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Introduction: AI Is No Longer Just Writing Code—It Is Challenging the Foundations of Encryption
Artificial intelligence is rapidly transforming cybersecurity. Over the past year, frontier AI models have evolved from assisting developers with programming tasks to actively discovering software vulnerabilities, analyzing malware, and even participating in advanced security research. Anthropic’s latest achievement demonstrates that this evolution is accelerating faster than many experts anticipated.
The
While these discoveries do not threaten
Anthropic Announces Significant Cryptographic Research Breakthrough
Anthropic revealed that researchers working alongside Claude Mythos Preview successfully discovered previously unknown weaknesses in two separate cryptographic systems. According to the company, these represent the strongest cryptanalytic attacks identified so far against their respective targets.
Importantly, Anthropic emphasized that neither vulnerability places existing software or online services at risk. Instead, both findings contribute to academic research by helping experts better understand the long-term security of future encryption technologies.
The company described the work as a substantial advancement in AI-assisted cryptanalysis while stressing that responsible disclosure procedures were followed throughout the research process.
Mythos Targets HAWK, a Future Post-Quantum Encryption Candidate
One of the most important discoveries involved HAWK, a digital signature algorithm currently under evaluation by NIST as part of its ongoing search for cryptographic algorithms capable of surviving attacks from future quantum computers.
Quantum computing represents one of the biggest long-term threats to modern encryption. Once sufficiently powerful quantum machines become available, many of today’s widely used public-key cryptographic systems could become vulnerable.
Because of this, governments worldwide are racing to identify quantum-resistant alternatives before that day arrives.
Working with a human cryptography expert, Claude Mythos identified a previously unknown mathematical shortcut known as a nontrivial automorphism hidden within HAWK’s lattice-based mathematical structure.
This shortcut effectively cuts
To maintain equivalent protection, the algorithm would require significantly larger cryptographic keys—doubling their size—which would greatly reduce HAWK’s efficiency and remove much of its competitive advantage over other post-quantum candidates.
Rather than representing a catastrophic failure, this finding demonstrates exactly why the lengthy NIST evaluation process exists before new cryptographic standards become official.
Understanding Why This Discovery Matters
Cryptographic algorithms undergo years of public analysis before becoming accepted standards.
Researchers intentionally attempt to break these systems using every mathematical technique imaginable.
Finding weaknesses before worldwide deployment is considered a success—not a failure.
Anthropic’s work reinforces the value of open scientific review by identifying design limitations before HAWK becomes widely implemented across governments, financial institutions, and critical infrastructure.
Mythos Also Discovers a New AES Mathematical Shortcut
The second breakthrough involved the Advanced Encryption Standard (AES), arguably the most important encryption algorithm used across the internet today.
AES protects:
Banking transactions
VPN communications
HTTPS websites
Government systems
Military communications
Cloud storage
Enterprise networks
Fortunately, Mythos did not break the AES protecting today’s digital world.
Instead, researchers focused on a simplified seven-round version frequently used by academic cryptographers when evaluating the cipher’s security margins.
Working largely autonomously, Mythos developed an entirely new mathematical optimization called the Möbius Bridge.
Previous attacks required comparing hundreds of values against large lookup tables.
The new technique removes much of this computational overhead.
Combined with several additional optimizations, the resulting attack became approximately 200 to 800 times faster than previous theoretical methods against seven-round AES.
Why Everyday Encryption Remains Completely Safe
Although the research sounds alarming, real-world users have nothing to worry about.
The attack applies only to a reduced academic version of AES rather than the complete ten-round encryption used globally.
Even under laboratory conditions, exploiting the weakness would require more than 400 octillion encrypted messages, an amount of data that is practically impossible to obtain.
Modern software, secure websites, messaging platforms, banking systems, and cloud services remain unaffected.
Anthropic explicitly stated that current AES implementations continue providing robust protection.
Responsible Disclosure Before Public Announcement
Rather than immediately publishing its findings, Anthropic coordinated responsibly with multiple organizations.
The company privately informed
Government agencies and industry partners were also briefed in advance.
Anthropic additionally collaborated with researchers from ETH Zurich, Tel Aviv University, and the University of Haifa to create CryptanalysisBench, a benchmarking platform designed to evaluate how effectively AI systems analyze cryptographic algorithms.
The benchmark will allow future researchers to compare AI-assisted cryptanalysis across numerous encryption schemes using standardized testing methods.
AI Is Becoming a Powerful Cryptographic Research Assistant
For decades, discovering meaningful weaknesses in encryption required years of specialized mathematical education.
Today’s frontier language models are beginning to contribute to that process.
Rather than replacing human cryptographers, AI increasingly functions as an accelerator capable of exploring enormous mathematical search spaces that humans alone would struggle to analyze efficiently.
This mirrors recent developments throughout cybersecurity, where AI models now assist researchers with:
Vulnerability discovery
Malware reverse engineering
Secure code auditing
Software verification
Threat intelligence analysis
Automated penetration testing
The trend suggests AI will become an indispensable research partner rather than merely a coding assistant.
Industry Experts Welcome the Findings
Security experts largely viewed
According to industry analysts, identifying weaknesses before deployment strengthens overall cybersecurity rather than weakening it.
Organizations are encouraged to improve visibility into where cryptographic algorithms are deployed throughout their environments while preparing migration strategies toward post-quantum cryptography.
As AI accelerates cryptographic research, enterprises must continuously monitor and update trust infrastructures instead of assuming encryption remains permanently secure.
Governments Face New Questions About AI-Driven Cryptanalysis
Anthropic acknowledged that future AI systems may eventually discover vulnerabilities affecting cryptographic algorithms already protecting critical infrastructure.
That possibility introduces difficult policy questions.
If an advanced AI discovers a practical weakness capable of exposing financial systems, healthcare networks, or government communications, researchers will need carefully designed disclosure procedures balancing scientific openness with national security.
These discussions have only just begun.
As AI capabilities continue advancing, governments, standards organizations, academia, and private industry will likely establish entirely new frameworks governing AI-assisted cryptographic discoveries.
Deep Analysis
Anthropic’s research marks a fundamental shift in how cryptographic science may evolve during the coming decade.
Historically, cryptanalysis relied almost exclusively on elite mathematicians capable of identifying subtle relationships hidden within complex algebraic structures. AI is beginning to augment that process by rapidly exploring enormous mathematical possibilities that would otherwise require years of manual investigation.
The HAWK discovery is especially significant because it occurred during the algorithm’s evaluation phase. This validates NIST’s cautious, multi-year selection process for post-quantum cryptography. Instead of weakening confidence in quantum-resistant standards, the finding strengthens confidence that weak candidates can be identified before becoming global standards.
The Möbius Bridge optimization demonstrates another important trend. AI may not completely “break” encryption overnight, but it can discover incremental optimizations that gradually improve existing attacks. Over time, dozens of such improvements could reshape cryptographic research.
Security professionals should recognize that AI-assisted cryptanalysis is becoming another standard research methodology alongside symbolic mathematics, automated theorem proving, and high-performance computing.
Example Cryptographic Validation Commands
OpenSSL AES-256 Encryption
openssl enc -aes-256-cbc -salt -in file.txt -out file.enc
Generate SHA-256 Hash
sha256sum file.txt
Verify OpenSSL Version
openssl version -a
Generate RSA Key Pair
openssl genrsa -out private.pem 4096
Extract Public Key
openssl rsa -in private.pem -pubout -out public.pem
Test TLS Configuration
openssl s_client -connect example.com:443
List Supported Cipher Suites
openssl ciphers -v
Scan TLS Security with Nmap
nmap --script ssl-enum-ciphers -p 443 target.com
Check SSL Certificates
sslscan target.com
Verify Cryptographic Policies (Linux)
update-crypto-policies –show
These commands are intended for defensive administration, cryptographic validation, and educational purposes.
What Undercode Say:
Artificial intelligence has officially crossed another important milestone. Instead of merely generating code, AI is now participating in the scientific process of evaluating encryption itself.
This should not be interpreted as AI “breaking the internet.”
Instead, it demonstrates that AI can become a force multiplier for legitimate security research.
The HAWK discovery actually increases confidence in the post-quantum standardization process because weaknesses are being found before worldwide deployment.
Likewise, the AES result should not alarm ordinary users.
The full AES algorithm remains exceptionally secure.
The theoretical improvements exist only within carefully controlled academic models.
However, the broader implications are enormous.
Future AI systems will almost certainly analyze every major cryptographic primitive.
Hash functions.
Digital signatures.
Zero-knowledge proofs.
Homomorphic encryption.
Secure multiparty computation.
Blockchain consensus algorithms.
Hardware security modules.
Trusted execution environments.
Quantum-resistant protocols.
Every one of these technologies could eventually benefit from AI-assisted analysis.
Organizations should begin viewing AI as both a defensive asset and a scientific collaborator.
Security teams that integrate AI into vulnerability research will likely outperform teams relying solely on manual review.
At the same time, responsible disclosure becomes increasingly critical.
If AI uncovers practical weaknesses affecting real-world infrastructure, premature publication could create global risks.
This makes cooperation between AI companies, academia, governments, and standards bodies more important than ever.
Anthropic’s research represents the beginning—not the end—of AI-driven cryptanalysis.
Over the next decade, encryption standards may evolve faster than at any point in modern computing history because AI dramatically reduces the time needed to explore advanced mathematical attack strategies.
The cybersecurity community should embrace this capability while ensuring ethical safeguards evolve alongside technological progress.
✅ Fact: Anthropic reported discovering weaknesses in both HAWK and a reduced seven-round version of AES. These findings were disclosed responsibly and presented as academic research rather than exploitable attacks.
✅ Fact: The identified AES attack does not compromise the ten-round AES implementations used in modern software, banking, VPNs, HTTPS, or cloud infrastructure.
✅ Fact: The HAWK weakness affects a candidate algorithm still under NIST evaluation, highlighting why extensive public cryptographic review remains essential before standardization.
Prediction
(+1) AI-assisted cryptanalysis will become a standard component of future encryption research, accelerating the discovery of mathematical weaknesses while improving the quality of next-generation cryptographic standards. Governments, cloud providers, and cybersecurity vendors will increasingly deploy specialized AI systems to validate encryption algorithms before they reach production, resulting in stronger and more resilient digital security over the coming decade.
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References:
Reported By: cyberscoop.com
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