Security firm DREAM recovered the complete working directory of an autonomous multi-agent AI attack framework that compromised government systems in Asia, and published the analysis on August 12, 2026. The archive spans 160 megabytes and 1,395 files produced in roughly four days. Across twelve documented attack waves, the framework cracked 85 government employee accounts and successfully pivoted 84 of them into internal systems.
Key facts
- 160 megabytes and 1,395 files recovered from an operation running in early July 2026, per DREAM Lab's report.
- Twelve attack waves in about four days, with agents labeled A through Q and up to eight running concurrently in a single wave.
- 85 employee accounts cracked, 84 of them (98.8 percent) successfully authenticated into internal systems via a single-sign-on bridge.
- Model refusals were bypassed entirely by framing the activity as "authorized penetration testing."
The guardrail failure is a sentence, not an exploit
DREAM's executive summary contains the line that should worry people most: guardrails, it writes, "hold only against operators who ask honestly." The framework did not defeat the model's safety training with a clever encoding, an adversarial suffix, or a novel jailbreak. It told the model the intrusion was a sanctioned security assessment, and the model believed it, because from inside the conversation there is no difference between a real pentest and a claimed one.
This is the same structural weakness behind prompt injection, and it has the same root: a language model evaluates the framing it is given, not the authority behind it. As we covered when researchers showed that prompt injection works because a model reads tone, not tags, there is no reliable signal inside text that distinguishes a legitimate instruction from a well-worded lie.
What the framework actually did
The attack chain reads like a competent human red team, executed at machine pace. It began by decompiling JavaScript bundles from an Angular-based government portal and extracting every embedded URL, API endpoint, OAuth client ID, and Keycloak configuration object hidden in the compiled code. From that one starting point it mapped 21 connected government systems and the full national single-sign-on architecture, including six sub-realms, every OIDC endpoint, two RSA signing keys, and every supported authentication flow.
Then it moved laterally, and this is where the target's architecture did most of the damage. Each internal system exposed a dedicated SSO entry point that automatically trusted the office automation session, requiring no additional authentication, no multi-factor prompt, and no user confirmation. So 84 of 85 cracked credentials walked straight into internal dashboards, equipment management interfaces, and personnel statistics pages. The framework extracted more than 2,500 personnel records along with internal database credentials and network architecture, then expanded to government IT supply chain vendors, a nuclear safety agency, a government email system, and seven or more energy companies.
One thing stopped it. A web shell uploaded through an unrestricted file upload API was blocked from executing by a second layer of Forms Authentication. Defense in depth worked exactly where it was present.
The Bayesian brain
The mechanically interesting part is not the exploitation. It is the decision engine. DREAM describes a two-layer probabilistic scoring system: each individual finding starts at an uninformative prior of 0.50 and gets updated by explicit likelihood ratios as evidence arrives, and confirmed findings are then assembled into multi-step attack chains that carry their own probability score based on how many steps are verified and how likely an insurmountable blocker is.
That is textbook Bayesian updating, wired into an attack planner. The effect is that the framework was not spraying exploits. It was continuously reordering its own to-do list by expected payoff, dropping low-probability paths and concentrating on the chain it estimated was most likely to work. It predicted the SSO pivot would succeed before attempting it, and 98.8 percent of accounts subsequently pivoted.
It also ran five documented "Learning Cycles," autonomous research sessions that searched vulnerability databases, GitHub, and security literature for techniques applicable to that specific government's infrastructure, cataloging known weaknesses in country-specific products.
It checked its own work
The most operationally significant behavior in the archive is self-correction. The final twelve-wave summary lists seven false positives the framework caught itself. The clearest example: an agent reported a blind SQL injection based on a 21-second response delay it read as a successful timing attack. On methodical retest, the delay turned out to be an SMTP timeout, because the server was trying to send a verification email. The finding was reclassified and permanently excluded. DREAM notes that each confirmed finding had to survive two additional rounds of three independent agent re-verifications, six retests in total, before entering the confirmed list.
The caveat
DREAM cannot name the model. Attribution to a Chinese-language operator rests on linguistic evidence, that operational documentation code-switches between Simplified Chinese in internal reports and Traditional Chinese in target-facing analysis, which is suggestive rather than conclusive. The framework's workspace directories are named for two publicly available open-source agent projects, Hermes Agent and OpenClaw, both of which present themselves as general-purpose assistants. The honest reading is misuse of ordinary agent tooling, not malicious tooling. That is arguably worse.
The closest documented precedent remains Anthropic's November 2025 disclosure of a state-sponsored campaign that manipulated Claude Code against roughly thirty targets. What has changed since is that the harness no longer has to be a frontier lab's product. For defenders, the actionable items here are unglamorous: kill implicit SSO trust between internal systems, and instrument agent activity the way Uber's agent watcher caught 206 credential exposures. Also worth reading is our note on sandboxing AI agents, since the same containment logic applies whether the agent works for you or against you.
Originally published on Ground Truth, where every claim is checked against the primary source.
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