Decepticon
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Decepticon is an autonomous red team agent that executes attack chains covering reconnaissance, exploitation, privilege escalationprivilege escalationGaining permissions beyond those an account or process initially had. It can turn limited access into administrator control or access to another user's resources., lateral movementlateral movementMoving from an initially compromised system or identity to other resources in a network. Attackers often use stolen credentials or trusted connections to expand their access., and C2.
decepticon update --channel latest decepticon update --channel latest decepticon decepticon --resume /scope import targets.txt --confirm /mode red approve decepticon doctor --json /mode red approve /mode blue # ... investigate sensor events ... /mode plan /resume /mode Red /mode Plan /mode Blue DECEPTICON_TELEMETRY=off decepticon onboard DECEPTICON_TELEMETRY=off docker build --build-arg VERSION=v1.0.16 . make dogfood DECEPTICON_STARTUP_TIMEOUT_SECONDS=120 decepticon decepticon remove --volumes --remove-orphans docker compose run --no-build decepticon WEB_PORT=8080 decepticon DECEPTICON_CLAUDE4_COMPAT=0 decepticon load_opplan(workspace_path) curl -fsSL https://raw.githubusercontent.com/PurpleAILAB/Decepticon/main/scripts/install.sh | bash && decepticon
Summary
Decepticon is an open-source autonomous red-team agent, licensed under Apache 2.0, that plans and executes penetration-testing engagements rather than just running individual scans. It ships as a CLI with a Docker-based sandbox for isolated tool execution, and the same project also runs as a hosted app for teams that don't want to self-host; either way it's aimed at red teamers and offensive security practitioners rather than defenders. It builds a knowledge graphknowledge graphA representation of entities and the relationships between them. Software can follow those connections to answer questions or combine information from different sources. of a target environment, feeding in BloodHound Community Edition data and AD objects alongside web and network recon tools, and plans engagements through a structured interview that produces rules of engagement, threat profiles, and abort criteria before any action runs. Plugin bundles let teams override agent roles and tool sets without forking the core. First committed in 2025, it has 26 contributors and over a thousand commits in the past year, with a release 14 days ago.
Decepticon is an autonomous red team agent that executes attack chains covering reconnaissance, exploitation, privilege escalation, lateral movement, and C2.
How Decepticon helps
Which hosts in scope actually get compromised, and how?
runs the full attack chain autonomously against the authorized target, moving from reconnaissance through exploitation, privilege escalation, lateral movement, and command-and-control without hand-holding each stage
What would an attacker do next once one machine is popped?
chains the stages so a foothold on one host automatically feeds targeting and lateral movement decisions for the next, rather than stopping after a single tool's output
Is a target actually authorized and scoped before an engagement starts?
the autonomous planning lane checks planning documents and scope against the stated target and only marks an engagement ready once that validation passes
Did the engagement follow rules I set, or wander off on its own?
runs under rules the user defines for the engagement, and separates an interview-first planner from the autonomous planning lane so the amount of autonomy given is a choice made up front
How much of my infrastructure and budget does a run consume while it's going?
lets the operator tune how many attack trajectories run at once and how much context each one gets, trading engagement speed against provider load and cost
Which findings came from real evidence versus a guess the agent made?
evidence controls attach severity ceilings, dependency reachability, and local API ingestion to each finding, and specialist validation contracts check the evidence before a result is reported
Languages
Command line
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Values are placeholders taken from each option’s declared default. Nothing is executed here — the output shown is a recording of a run that already happened.
Score card
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Shipping velocity releases a month, last 12 months
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Documentation quality release-notes rubric, 0–100
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Breaking-change discipline how rarely a release breaks users, and notice given
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Maintenance risk quiet time against its own cadence, maintainers
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Innovation & surface new capabilities, API and MCP surface
- Community health
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Contributor trend distinct committers, this year against last
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Maintainer concentration bus factor now against a year ago
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Issue backlog trend issues closed per issue opened, 12 months
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Responsiveness median time to a maintainer’s first reply
Nine scores for Decepticon, each built from the releases, notes and API history this site tracks — and, on Researcher and Business, where it stands among every measured tool in its category and how that has moved.
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Blank means never measured; nothing here is a filled-in zero.
Peer standings are included with Researcher and Business. See how this tool ranks against its competitors, using data we don't publish anywhere else.
See plans →Each score is 0–100 on a published rubric, derived from data this site already shows. A dimension we cannot measure says so — it is never scored 0 and never enters a rank.
How this is scored
- Shipping velocity — releases a month over the last 12 months, on a log scale where 16 a month scores 100. Needs 3 releases in the window.
- Documentation quality — the release-notes rubric published on the analytics page, unchanged.
- Breaking-change discipline — the share of releases carrying a breaking change (none scores 100, half or more scores 0). Only projects that label breaking changes are measured: a clean record cannot be told from an unlabelled one.
- Maintenance risk — quiet time measured against the tool's own release cadence (quiet longer than 90 days and three typical gaps scores 0), plus the number of active maintainers and whether contributors are growing, where known. Higher is lower risk.
- Innovation & surface — new capabilities in the feature ledger this year, API and MCP operations added, and the share of commits that are features: the mean of whichever we hold.
- Community health (open-source tools only) — each is a direction, not a size: contributors this year against last, the bus factor now against a year ago, and issues closed per issue opened, where steady scores 50, doubled 100 and halved 0; plus the median time to a maintainer's first reply (an hour scores about 100, a month or more 0). A commercial card has no repository and carries none of the four.
- Documented delivery (commercial tools) — when a vendor's documented new capabilities have slowed against its own baseline, that counts against the overall standing as heavily as two low dimensions. Steady delivery, or too little history to judge, leaves the standing alone; it is never a sixth score.
- Open-source tools are scored on repository signals; commercial tools on their published changelog. Ranks compare tools within one category and one profile and need at least five measured peers; the overall standing needs three ranked dimensions. Best-in-breed or consolidator is a shape, not a quality, and is never scored.
Release-note detail over time
See how much detail Decepticon puts in its release notes, quarter by quarter, and compare it with tracked tools in the same category. This history uses up to ten retained, reviewed notes per tool; sparse or blank older quarters may reflect that limit.
Checking release-note history…
Releases
23 entries
No entries match these filters.
- October 20265
- September 20261
- July 20261
- June 20264
- May 20262
- April 20268
- March 20262
Project health
Issue, contributor and maintainer activity in PurpleAILAB/Decepticon, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.
- Contributors 20 +20 vs prior 12 months
- Maintainers 5 +5 joined · −0 left, last 12 months
- Bus factor 3 committers behind half the commits
- Community-filed 65% of issues opened, last 12 months
- Median time to close 2 days first maintainer reply 37 hours
- Open backlog 1 of 87 issues ever filed
- Issues opened / month 0 · 0 communitymaintainers
- Open backlog 1
- Contributors / month 4
- Commits / month 9
- Stars 5,557
- bugs 49%
- features 13%
- questions 1%
- other 37%
Counts from the project's public repository and git history; no individuals are identified. Maintainers are the committers covering 80% of the last year's commits. how we measure this
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