apt

SiegedSec

defunctAttribution: ModeratePlausible and partly corroborated — some gaps or single-source elements.

APT / State-sponsored

Gay Furry HackersSiegedSec CollectiveSieged Security

Attribution

Loosely-organized ideologically-motivated hacktivist collective, not a state-nexus actor. Fronted publicly by an individual using the alias "vio." No credible technical or law-enforcement attribution to a nation-state; membership and geography never authoritatively established.

First seen

2022-04

Last active

2024-07

Motivation

Politically- and socially-motivated hacktivism (leak-and-shame). Campaigns framed around US culture-war and civil-rights issues (pro-choice/anti-abortion-law, pro-trans/anti-anti-trans policy, opposition to Project 2025), anti-NATO/anti-establishment provocation, pro-Palestinian/anti-Israel activism during the 2023-2024 Gaza conflict, and notoriety/lulz. Self-described "gay furry hackers."

Attribution confidence

medium

MENA targeting

Israel

Sectors

Retail/grocery, aviation, government

Why it mattersState-sponsored / APT actor, medium confidence, documented targeting Israel (Retail/grocery, aviation, government sectors).

What's nextNo pipeline reports reference this actor yet — Radar mentions may still surface early signal.

Associated with

Related to

CyberAv3ngersactorTechnique overlap
KillSecactorTechnique overlap
Void Manticore (Storm-0842)actorTechnique overlap

Uses

Data from Information RepositoriestechniqueATT&CK mapping
Cloud AccountstechniqueATT&CK mapping
Network Denial of ServicetechniqueATT&CK mapping
Vulnerability ScanningtechniqueATT&CK mapping
Exploit Public-Facing ApplicationtechniqueATT&CK mapping

Coverage robustness · vs this actor

How evasion-resistant our detection coverage is against SiegedSec's techniques — Summiting the Pyramid scores analytics (1 brittle – 5 robust); the actor inherits through its TTPs. This is a detection-engineering metric, not an attribution signal — see Attribution for confidence.

Techniques with a robust rule

Techniques with any detection

4 / 7

Mean robustness (of covered)

2.3

Useful?