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Introduction:
The latest viral trend—asking ChatGPT to “create a caricature of me and my job based on everything you know about me”—has swept across Instagram and LinkedIn, with over 2.6 million images posted in early February 2026 alone . While users view it as harmless fun, cybersecurity professionals see it differently: a perfect storm of Shadow AI, data leakage, and targeted social engineering. By participating, you are not just sharing a cartoon; you are broadcasting to attackers that you use large language models (LLMs) at work, potentially exposing sensitive corporate data, authentication tokens, and personal identifiers that can be exploited for LLM account takeover, credential theft, and sophisticated phishing campaigns .
Learning Objectives:
- Understand how the AI caricature trend expands the attack surface through OSINT and LLM account compromise.
- Learn the technical mechanics of harvesting authentication tokens from desktop AI applications (AIKatz attack).
- Identify mitigation strategies including endpoint hardening, IAM controls, and prompt hygiene for both individuals and enterprises.
You Should Know:
- The Anatomy of the AI Caricature Threat: Reconnaissance and Signal Amplification
When a user prompts an LLM with “everything you know about me,” they are essentially performing a bulk data export of their digital footprint stored by the AI. This includes not only the uploaded photo but also historical chat data, remembered preferences, and potentially work-related information previously shared . Once the caricature is posted publicly, it becomes a high-value signal for attackers.
Fortra security analyst Josh Davies notes that these posts allow attackers to easily identify high-value targets—bankers, engineers, HR employees, and healthcare workers—by simply scrolling through public feeds . The attacker then correlates the social media username with the LLM-generated image to perform doxing. Using open-source intelligence (OSINT), they can often derive the victim’s email address, which is likely the same one used for the LLM account.
Step‑by‑step guide: Attackers’ Recon Workflow
For educational and defensive purposes, here is how an adversary might automate this reconnaissance:
Example OSINT gathering using command-line tools (Linux) 1. Use Sherlock to find usernames across social networks python3 sherlock --timeout 2 --print-found USERNAME <ol> <li>Use theHarvester to gather email addresses associated with a domain theHarvester -d company.com -b all</p></li> <li><p>Cross-reference email addresses with known breach databases using haveibeenpwned API curl -H "hibp-api-key: YOUR_KEY" https://haveibeenpwned.com/api/v3/breachedaccount/[email protected]</p></li> <li><p>Simulate phishing link creation (for testing only) setoolkit Choose Social-Engineering Attacks > Credential Harvester Attack Method
Once the email is identified, the attacker sends a credential-harvesting link. If successful, they gain access to the LLM account and can browse the entire prompt history for sensitive corporate data—financial projections, source code, or internal strategy documents .
- Technical Deep Dive: AIKatz and Local Token Harvesting
Beyond social engineering, a more sophisticated attack vector targets the desktop applications of AI services like ChatGPT, Claude, and Microsoft Copilot. Lumia Security’s research on “AIKatz” demonstrates how attackers, after gaining local machine access, can extract authentication tokens from these applications .
All three major AI desktop apps are built on Chromium-based frameworks (Electron for ChatGPT/Claude, WebView2 for Copilot). Chromium stores authentication tokens and cookies in process memory. Using techniques similar to Mimikatz, attackers can scan the memory of these processes for JSON Web Tokens (JWTs) or session cookies.
Step‑by‑step guide: Extracting Tokens from AI Desktop Apps (Proof of Concept)
Note: This is for educational and defensive testing only.
On a compromised Windows machine, an attacker might use a tool to dump process memory and grep for token patterns.
Windows PowerShell (Admin) - Dump ChatGPT process memory
Find the process ID for ChatGPT
Get-Process -Name "ChatGPT" | Format-Table Id, ProcessName
Use Procdump from Sysinternals to dump memory
.\procdump64.exe -ma 12345 chatgpt_memory.dmp
Search for JWT patterns (base64url encoded, typically starting with eyJ)
Select-String -Path .\chatgpt_memory.dmp -Pattern "eyJ[A-Za-z0-9_-]{10,}.[A-Za-z0-9_-]{10,}.[A-Za-z0-9_-]{10,}" | Out-File -FilePath tokens_found.txt
For Claude, search for session cookie pattern
Select-String -Path .\claude_memory.dmp -Pattern "sk-ant-[A-Za-z0-9]+" | Out-File -FilePath claude_tokens.txt
On Linux (for Electron apps), an attacker could use `gdb` or gcore:
Linux memory extraction
gcore $(pidof chatgpt)
strings core. | grep -E "eyJ[A-Za-z0-9_-]{20,}.[A-Za-z0-9_-]{20,}.[A-Za-z0-9_-]{20,}"
Once the token is extracted, the attacker can import it into their own browser session and gain full access to the victim’s conversation history, memories, and settings. They can even perform prompt injection or delete conversations without triggering alerts on the victim’s device .
3. LLMjacking: Cloud-Scale Exploitation of AI Credentials
The risk extends beyond individual desktop users to enterprise cloud environments. Entro Security’s research on LLMjacking reveals how attackers exploit exposed non-human identities (NHIs)—such as AWS API keys—to gain unauthorized access to GenAI models like Anthropic Claude or Amazon Bedrock .
In their honeypot experiment, exposed AWS keys were accessed within an average of 17 minutes. Attackers first performed reconnaissance using API calls like `GetCostAndUsage` to assess the account’s value, followed by `ListFoundationModels` to enumerate available AI services. Only then did they attempt model invocation, which could cost victims up to $46,000 per day in unauthorized usage fees .
Step‑by‑step guide: Detecting LLMjacking Reconnaissance
Defenders should monitor CloudTrail logs for suspicious patterns:
AWS CLI command to search for reconnaissance API calls
aws cloudtrail lookup-events --lookup-attributes AttributeKey=EventName,AttributeValue=GetCostAndUsage --start-time 2026-02-01 --end-time 2026-02-15
Check for ListFoundationModels calls from unfamiliar IPs
aws cloudtrail lookup-events --lookup-attributes AttributeKey=EventName,AttributeValue=ListFoundationModels
Use jq to extract source IPs and user agents
aws cloudtrail lookup-events --query 'Events[?EventName==<code>InvokeModel</code>].[bash]' --output text | jq -r '.userIdentity.sessionContext.attributes | {ip: .sourceIpAddress, ua: .userAgent}'
If an attacker has compromised a key, they will often invoke models using the AWS SDK. A sudden spike in `InvokeModel` API calls from a geographic region where your organization has no presence is a critical indicator of compromise.
4. Identity Fraud and Social Engineering Amplification
The caricature trend also facilitates traditional identity fraud. By providing a visual representation of the user, along with contextual clues about their job and lifestyle, attackers can craft highly convincing spear-phishing messages. Bob Long of Daon compares this to the “40 things about me” social media chains, where users voluntarily compiled dossiers on themselves .
This information can be used to bypass knowledge-based authentication (KBA) questions, such as “What city were you born in?” or “What is your mother’s maiden name?”—details often implied by the AI-generated caricature or the user’s prompt history.
Step‑by‑step guide: Hardening Against KBA Bypass
Organizations should move away from KBA and implement phishing-resistant MFA. For Microsoft environments, this can be enforced via Conditional Access:
Connect to Microsoft Graph PowerShell
Connect-MgGraph -Scopes "Policy.ReadWrite.ConditionalAccess"
Create a Conditional Access policy requiring phishing-resistant MFA for all users
$params = @{
DisplayName = "Require Phishing-Resistant MFA for AI App Access"
State = "enabled"
Conditions = @{
Applications = @{
IncludeApplications = @("All")
}
Users = @{
IncludeUsers = @("All")
}
}
GrantControls = @{
BuiltInControls = @("phishingResistantMfa")
Operator = "OR"
}
}
New-MgIdentityConditionalAccessPolicy -BodyParameter $params
Additionally, deploy endpoint detection and response (EDR) tools to monitor for process memory dumping activities, which are key indicators of token theft attempts.
5. Governance and the Shadow AI Problem
The caricature trend is a symptom of a larger issue: Shadow AI. Employees are using personal LLM accounts for work tasks without IT’s knowledge, exposing corporate data to unmanaged environments . The OWASP LLM Top Ten lists Sensitive Information Disclosure (LLM2025:02) as a critical risk, and this trend exemplifies how easily it occurs.
Organizations must implement AI governance policies that include:
- Blocking unsanctioned AI applications via secure web gateways.
- Providing approved, enterprise-managed AI instances with data loss prevention (DLP) controls.
- Training employees on prompt hygiene—avoiding sharing PII, financial data, or source code with public LLMs.
Configuration Example: Blocking Consumer AI Apps via Firewall
Linux iptables example to block outbound traffic to OpenAI API (for non-corporate use) iptables -A OUTPUT -d api.openai.com -j DROP iptables -A OUTPUT -d anthropic.com -j DROP For Windows Firewall with Advanced Security New-NetFirewallRule -DisplayName "Block ChatGPT" -Direction Outbound -RemoteAddress "104.18.20.121" -Action Block
However, blocking alone is insufficient; organizations must provide secure alternatives to prevent users from tunneling or using personal devices.
What Undercode Say:
- Key Takeaway 1: The caricature itself is not the vulnerability—it is the signal. The real risk lies in the confirmation that a user actively employs LLMs, the public exposure of their digital identity, and the potential that their prompt history contains proprietary data. Attackers now have a searchable database of high-value targets.
- Key Takeaway 2: Endpoints and credentials are the new battleground. Whether through local token harvesting (AIKatz) or cloud-based LLMjacking, the authentication mechanisms of AI services are becoming prime targets. Traditional perimeter defenses fail against attacks that abuse valid credentials and application memory.
In analysis, this trend underscores a fundamental shift: AI assistants have become repositories of sensitive corporate and personal data, yet they remain largely ungoverned. The average user lacks AI literacy regarding data retention, memory features, and the permanence of their prompts . Until organizations treat AI accounts with the same rigor as email or CRM systems, and until vendors implement stronger client-side security (e.g., protected process memory, hardware-bound tokens), these attacks will proliferate.
Prediction:
Over the next 12 months, we will see the first major breach directly attributable to the AI caricature trend. Attackers will automate the correlation of public LLM-generated images with leaked credential databases, leading to a wave of LLM account takeovers. This will force regulatory bodies to update data protection guidance, specifically addressing the use of personal AI accounts for work purposes. Expect the emergence of “AI Firewall” products that monitor and block sensitive data from being sent to unauthorized LLM endpoints, as well as OS-level controls to prevent memory scraping of AI applications. The era of frictionless, ungoverned AI use in the enterprise is ending.
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Reported By: Michael Tchuindjang – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
