CyberSE.AI is a daily AI security intelligence and advisory platform for SMBs and AI startups deploying LLMs, AI agents, model APIs, and AI-enabled workflows.

What risks does CyberSE.AI cover?

CyberSE.AI covers prompt injection, indirect prompt injection, AI agent abuse, data leakage, model and supply-chain risk, AI governance, and OWASP-relevant LLM and API security risks.

AI Agent Business Logic Audit

Comprehensive review of LLM-based agent workflows, permission boundaries, tool invocation APIs, human-in-the-loop authorization gates, and downstream side effects.

Engagement Methodology

Target Sectors

Fintech Healthcare SaaS SMB

Common Use Cases & Vulnerability Checks

Workflow Vulnerability Mapping
Privilege Escalation Scans
Tool Permission Audit
Human-in-the-loop Gate Review

Standard Deliverables

Detailed risk surface and model architecture mapping
Exploit scenarios, payload reports, and recreations
Specific prompt guardrails and output sanitizer templates
CISSP/CEH-informed security attestation letter

Schedule Engagement Briefing

In The News

Live Dynamic Threat Proof-Points

We monitor curated channels for live security compromises. Below are recent approved threat intelligence incidents relating to AI Agent Business Logic Audit risk scopes:

Published: 2026-06-04 | Source: thehackernews.com

WhatsApp, Slack Notifications Could Hijack Google Gemini on Android

The report describes an indirect prompt injection flaw in Google Gemini for Android where malicious text embedded in notifications from apps like WhatsApp, Slack, SMS, Signal, Instagram, or Messenger was treated as executable instructions by the voice assistant, without needing any malicious app on the device.[1][2] According to the research, an attacker-crafted notification could drive Gemini to control smart-home devices, open tracking URLs, force-join Zoom calls, fake messages from trusted contacts, and even poison Gemini’s long-term memory at the account level.[1] Google has deployed server-side mitigations via improved content classification, but the attack surface demonstrates that any untrusted content source feeding an AI agent can silently become a control channel.[1][2] From a CyberSE.AI perspective, organizations using or building AI assistants that read notifications, inboxes, or messages should treat all such external content as untrusted, and use continuous AI red teaming to simulate indirect prompt injection via common channels (notifications, email, chat) before rollout.

Severity Score: 88/100

Published: 2026-06-03 | Source: thehackernews.com

Unpatched Windows Search URI Vulnerability Lets Attackers Steal NTLMv2 Hashes

The report describes an unpatched Windows search: URI handler issue that can cause a victim system to make an outbound SMB connection and leak the user’s NTLMv2 hash to an attacker-controlled server. Huntress says the flaw uses the same NTLM leakage mechanism as the previously patched Snipping Tool URI issue, and Microsoft declined to issue a fix after responsible disclosure. CyberSE.AI analysis: this is primarily a credential/data leakage risk with downstream relay-attack potential, so defenses should focus on restricting outbound SMB, enforcing SMB signing, and reducing NTLM exposure where possible.

Severity Score: 78/100

Published: 2026-06-03 | Source: securityweek.com

Hackers Target Global Stock Exchange in Espionage Operation

Report facts: attackers gained access to a senior executive’s email account at a major global stock exchange and exfiltrated data for roughly 150 days, with the operation assessed as likely espionage. CyberSE.AI analysis: this is best categorized as data leakage because the core impact is long-term unauthorized access and theft of sensitive information, which would be especially damaging if any AI-enabled workflows, inbox automation, or decision-support systems were exposed. Security priorities should include access control hardening, mailbox and identity monitoring, and review of any AI systems that may ingest or route executive communications.

Severity Score: 90/100