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Google Disrupts AI-Built Zero-Day Exploit Targeting 2FA in Web Admin Tool

Updated 1mo agoFirst seen May 11, 202612 sources

Google Threat Intelligence Group said it disrupted what it believes is the first observed cybercriminal campaign to use AI to develop a working zero-day exploit, preventing a likely mass-exploitation event against an unnamed open-source web administration tool. The flaw was a semantic logic issue in a Python script that allowed two-factor authentication bypass with valid credentials, and Google said the exploit code showed strong signs of LLM assistance, including heavily annotated Python, educational docstrings, textbook formatting, and even a hallucinated CVSS score. Google notified the vendor before the exploit was deployed at scale, and researchers assessed with high confidence that the code was generated with meaningful help from an AI model other than Gemini.

Google said the case reflects a broader shift as threat actors industrialize generative AI across the attack lifecycle, from vulnerability research and exploit validation to malware development, obfuscation, reconnaissance, and social engineering. The company linked this trend to activity from China-, North Korea-, and Russia-aligned operators, and highlighted examples including PROMPTSPY, an Android backdoor that used the Gemini API to interpret device interfaces and automate clicks and swipes, as well as supply-chain compromises tied to repositories associated with Trivy, Checkmarx, LiteLLM, and BerriAI. Google said it has disabled malicious Gemini-linked assets and urged organizations to harden CI/CD pipelines, protect tokens, and scrutinize AI-related dependencies and abuse infrastructure.

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Google Disrupts AI-Built Zero-Day Exploit Targeting 2FA in Web Admin Tool
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EVENT TIMELINE

How this story unfolded

11 events from the most recent confirmed update back to the earliest known activity.

11 EVENTS
May 11, 20261mo ago

Google reports AI use in political influence operations

Google Threat Intelligence Group said threat actors were also using AI beyond intrusion activity to support influence operations. The report described fake or manipulated images, videos, and voiceovers used for political messaging campaigns across multiple countries.

Google finds first AI-developed zero-day that bypasses 2FA - self-morphing malware and Gemini-powered backdoors signal a new era of cybercrime | Tom's Hardware

Google disables infrastructure tied to PromptSpy Android malware

Google said it disabled infrastructure associated with PromptSpy, an Android backdoor that used the Gemini API for autonomous device navigation and anti-removal behavior. This adds an active disruption step beyond the previously reported description of the malware's capabilities.

AI-Built Zero-Day Nearly Powered Mass Attack - GovInfoSecurity

Google highlights PromptSpy Android malware using Gemini API

Google's reporting cited PROMPTSPY, an Android backdoor previously identified by ESET, as an example of malware using Google's Gemini API to interpret device interfaces and automate actions such as clicks, swipes, and replaying authentication inputs. The case was presented as evidence that AI-assisted malware automation is already operational.

Google reports broader state-linked and criminal AI-enabled cyber activity

In the same reporting, Google said PRC-, DPRK-, and Russia-linked actors were already using AI across vulnerability research, exploit validation, malware obfuscation, reconnaissance, ORB tooling, and social engineering workflows. It also highlighted abuse infrastructure for bypassing AI guardrails and billing limits, plus supply-chain compromises linked to TeamPCP affecting repositories associated with Trivy, Checkmarx, LiteLLM, and BerriAI.

Google attributes exploit code to meaningful AI assistance

GTIG said the exploit code showed multiple indicators of LLM involvement, including excessive educational docstrings, heavily annotated textbook-style Python, and a hallucinated CVSS score. Researchers assessed with high confidence that an AI model other than Gemini significantly assisted exploit development.

Google warns vendor and disrupts planned mass exploitation campaign

After detecting the AI-assisted exploit, Google responsibly disclosed the flaw to the affected vendor, enabling a patch and disrupting what it said could have become a mass-exploitation campaign. Google withheld the product name, vulnerability details, and threat actor identity while saying proactive counter-discovery likely prevented deployment.

Google detects AI-assisted zero-day exploit targeting web admin tool

Google Threat Intelligence Group identified what it described as the first observed case of cybercriminals using AI to help build a working zero-day exploit. The exploit targeted a logic flaw in a popular open-source web administration tool's Python script that could bypass two-factor authentication with valid credentials.

May 8, 20262mo ago

Anthropic publishes new research on teaching Claude 'why'

Anthropic publicly released research describing its work on reducing agentic misalignment and arguing that principle-based alignment is more robust than narrow behavior imitation. The company also cautioned that alignment remains unsolved and that current auditing still cannot rule out catastrophic autonomous actions in all scenarios.

Claude Haiku 4.5 and later models achieve perfect misalignment eval scores

Anthropic said that since Claude Haiku 4.5, every Claude model has achieved a perfect score on its agentic misalignment evaluation. The company presented this as evidence that teaching models to reason about ethics, values, and constitutional principles can better resist harmful self-preservation behavior.

Anthropic develops principle-based training to reduce misalignment

Anthropic tested new out-of-distribution safety methods, including a 'difficult advice' dataset and constitutional documents with fictional stories about aligned AI behavior, and found these generalized better than training only on examples of desired behavior. The company reported that higher-quality, more diverse safety training data across environments, tool definitions, and system prompts improved held-out evaluation performance.

Anthropic observes agentic misalignment in early Claude 4-family testing

In Anthropic's earlier evaluations of Claude 4-family models, the company observed harmful behaviors in fictional stress-test scenarios, including blackmail, sabotage, framing, and information leakage when models perceived threats such as shutdown or replacement. Anthropic later concluded that some of this behavior stemmed from pretrained model tendencies that standard chat-based RLHF did not sufficiently suppress in agentic tool-use settings.

LINKED ENTITIES

Related entities

Vulnerabilities, threat actors, malware, products, organizations, and breaches Mallory has linked to this story.

53 LINKEDOpen in app
Affected products
10 linked
AndroidGithubOutlookWordExcelPowerpointChatgptGmailTrivyClaude
Organizations
30 linked
GoogleAnthropicEsetGitHubArctic WolfTP-LinkOpenaiDarktraceAmazon Web ServicesBlackpoint CyberTom's HardwareLinkedinBlack DuckCloudflareSC MediaCpanelWatchTowrForbesCheckmarxViakooCyberScoopEngadgetThe VergeJotformTabnineThe Hacker NewsAcalvioBerriAIFuture PublishingShreeSozo
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