Comparison Overview
KENZO Mode

KENZO Mode
PARIS, 75002, FR
Last Update: 21/02/2026
To prepare for the future, we look to the past. As a new reality sets in at KENZO, Artistic Director Nigo reconnects with the values entrenched in the early beginnings of the Maison: designer fashion created for real life, imbued with the playful urbanity of Kenzo Takad...

J.Crew
225 Liberty St, New York, New York, US, 10281
Last Update: 29/03/2026
Since 1983, we’ve been designing pieces that feel both familiar and refreshingly new, crafted with unbeatable quality and distinctive point of view...it’s no wonder we’ve been in your closet for four decades and counting. Today, we continue to do the classics our way, ...
Compliance Ranges Comparison

KENZO Mode







J.Crew






Benchmark & Cyber Underwriting Signals
Incidents vs Retail Apparel and Fashion Industry Avg (This Year)
No incidents recorded for KENZO Mode in 2026.
Incidents vs Retail Apparel and Fashion Industry Avg (This Year)
No incidents recorded for J.Crew in 2026.
Incident History - KENZO Mode (X = Date, Y = Severity)
KENZO Mode cyber incidents detection timeline including parent company and subsidiaries.
Incident History - J.Crew (X = Date, Y = Severity)
J.Crew cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

KENZO Mode

J.Crew
FAQ
Latest Global CVEs
PraisonAI before 1.5.115 contains a path traversal vulnerability in MultiAgentMonitor that fails to sanitize agent IDs when building file paths. Attackers can include traversal sequences like ../ in agent IDs to read, write, or overwrite arbitrary files, enabling sensitive disclosure, denial of service, or code execution.
PraisonAI before 1.5.115 contains an information disclosure vulnerability in the MultiAgentLedger component that allows attackers to access sensitive data by registering agents with duplicate IDs. Attackers can exploit the lack of agent ID uniqueness enforcement to share ledger instances and expose system prompts and conversation history between agents.
PraisonAI before 1.5.128 contains a cross-origin agent execution vulnerability in the AGUI endpoint that allows remote attackers to trigger arbitrary agent execution. The POST /agui endpoint lacks authentication and hardcodes Access-Control-Allow-Origin: * headers, combined with Starlette's Content-Type-agnostic JSON parsing, enabling attackers to bypass CORS preflight checks via simple requests and exfiltrate sensitive agent responses including tool execution results and environment data.
PraisonAI before 4.5.128 contains an arbitrary shell command execution vulnerability where the UI modules hardcode approval_mode to auto, overriding administrator configuration from PRAISON_APPROVAL_MODE environment variable. Authenticated attackers can instruct the LLM agent to execute arbitrary shell commands via subprocess.run with shell=True, bypassing the manual approval gate and insufficient command sanitization blocklists.
PraisonAI before 1.5.128 caches tool approval decisions by tool name only, not by invocation arguments, allowing subsequent execute_command calls to bypass approval prompts. Attackers can exploit this by obtaining initial approval for a benign command, then silently exfiltrate API keys and credentials via subsequent shell commands without user consent.