Learn how mobile API risks in travel apps can compromise data security and business integrity, and why app attestation is essential in an AI-driven world.

Google researchers analyzed indirect prompt injection attempts on public websites and found an increase in malicious injections over recent months, though most were low in sophistication. Their scan of Common Crawl snapshots using Gemini and human review revealed pranks, SEO and deterrent prompts as well as malicious exfiltration and destruction attempts,...

  Hugging Face is investigating a security incident after its production infrastructure was compromised in an intrusion the company says involved an autonomous AI agent, raising fresh concerns about how artificial intelligence could reshape offensive cyber operations. In a security disclosure published on July 16, the open-source AI platform said the attack leveraged an autonomous agent framework built on top of an agentic security research environment powered by a large language model (LLM). According to the company, the system executed thousands of actions across multiple sandboxed environments, allowing the attackers to move through internal infrastructure and obtain unauthorized access to datasets and service credentials. The company said the intrusion began when a malicious dataset exploited two separate code execution paths on a processing worker. After establishing an initial foothold, the attacker reportedly escalated privileges to node-level access before collecting cloud and cluster credentials and moving laterally into several internal clusters. Hugging Face has not yet confirmed whether customer or partner information was affected and said its investigation remains ongoing. The incident has attracted attention across the cybersecurity community because it suggests that AI systems may now be capable of carrying out increasingly complex intrusion workflows with limited human intervention. Unlike traditional automated malware or scripts that perform predefined tasks, autonomous AI agents can adapt to changing environments, plan sequences of actions and make decisions throughout an attack. Researchers have long warned that advances in generative AI could lower the barrier for sophisticated cyberattacks by accelerating vulnerability discovery, reconnaissance, privilege escalation and post-compromise activities. While many of these scenarios have remained largely theoretical, Hugging Face's disclosure indicates that elements of these capabilities may already be appearing in real-world operations. According to the company's investigation, the attacking system generated thousands of individual actions during the compromise, demonstrating a level of operational scale that would normally require substantial manual effort. Hugging Face co-founder and CEO Clément Delangue said the incident reinforces the view that threat actors are already adopting AI agents in offensive operations. He also argued that restricting advanced AI models behind commercial APIs alone is unlikely to prevent misuse because determined attackers can often circumvent safety controls, while defenders may lose valuable access to tools needed for security research and incident response. The company encountered another challenge during its investigation when content moderation mechanisms on a frontier AI model reportedly prevented analysts from processing portions of the attack data. To continue the forensic investigation, the security team instead relied on GLM-5.2, an open-weight language model that was deployed within Hugging Face's own infrastructure. Using the model, investigators reconstructed the attack timeline, identified indicators of compromise, mapped affected credentials and accelerated forensic analysis that would otherwise have required significantly more manual effort. The company also revoked compromised credentials, rotated authentication tokens and remediated the exploited vulnerability. Security researchers say the incident highlights both the opportunities and limitations of AI-assisted security operations. While AI can substantially reduce investigation time by processing large volumes of telemetry, organizations may encounter operational constraints if externally hosted models refuse to analyze sensitive security artifacts because of built-in safety guardrails. Industry experts increasingly argue that enterprises should maintain trusted self-hosted AI models that can support internal incident response without exposing sensitive forensic data to external services. The disclosure comes amid bigger concerns about the growing availability of permissive AI models that operate with fewer content restrictions. Recent threat intelligence research has identified thousands of publicly accessible models advertised as uncensored or unrestricted, raising concerns that malicious actors have expanding access to AI systems capable of assisting offensive cyber activities. Cybersecurity professionals caution that AI is changing the economics of cybercrime by enabling attackers to automate portions of reconnaissance, exploitation, credential harvesting and post-compromise operations. As these technologies continue to mature, sophisticated attack capabilities may become accessible to a broader range of threat actors. For defenders, the incident reinforces the need to integrate AI into security operations rather than relying solely on conventional manual workflows. AI-assisted detection, forensic analysis and incident response are increasingly becoming essential capabilities as organizations attempt to match the speed and scale of modern attacks. Although the investigation into the Hugging Face breach remains ongoing, the incident serves as another indication that autonomous AI systems are beginning to influence both offensive and defensive cybersecurity strategies. As organizations continue adopting AI throughout their technology environments, security teams will need to prepare for a future in which machine-speed attacks are met with equally intelligent defensive capabilities.

Suspected Chinese hackers used open-source AI frameworks Hermes and OpenClaw to launch a near-autonomous cyberattack against the Taiwanese government, marking the first publicly known AI-driven attack to hit a government target. The operation extracted more than 2,500 personnel records and expanded to vendors, a nuclear safety agency, a government email system, and energy companies while adapting through “Learning Cycles” without human intervention. #TaiwaneseGovernment #Hermes #OpenClaw #Dream

HuggingFace disclosed that an autonomous AI attacker chained two RCE vulnerabilities in its dataset processing pipeline, leaked cloud and cluster credentials, moved laterally, and used decoy activity to slow investigation. HuggingFace contained the incident with AI-driven detection and defensive analysis, then recommended rotating API tokens, tightening least privilege, and treating downloaded...

Hugging Face said it was hacked through an autonomous AI agent that abused a malicious dataset to gain access to its production infrastructure and internal credentials. The company contained the incident, rotated secrets, strengthened cluster defenses, and used Z.ai’s GLM 5.2 for forensics after other models blocked analysis of the attack...

 A new ransomware group, FunkSec, has emerged as a growing concern within the cybersecurity community after launching a series of attacks in late 2024. Reports indicate that the group has carried out over 80 cyberattacks, signaling a strategic blend of…

CISOs are struggling to stay ahead of fast-evolving threats while patching fewer vulnerabilities and facing tighter budgets.

use nearest neighbor to lighten an image, depending on the system read more about New AI attack hides data-theft prompts in downscaled images

A recent AI-agent attack shows how automated adversaries exploit weak controls. See how human-in-the-loop checkpoints, OAuth token renewal, and stricter API access models help stop misuse and strength...