Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-23842 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability in Chatterbot Chatterbot. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique OS Exhaustion Flood (T1499.001); ranked at the 41th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Machine Learning Libraries; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-23842 is a denial-of-service vulnerability in ChatterBot, a machine learning conversational dialog engine used for creating chat bots. Versions up to 1.2.10 are affected due to improper database session and connection pool management. Specifically, concurrent invocations of the get_response() method exhaust the underlying SQLAlchemy connection pool, causing persistent service unavailability that requires a manual restart to recover. The issue is classified under CWE-400 (Uncontrolled Resource Consumption) with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).
The vulnerability can be exploited remotely over the network by unauthenticated attackers with low complexity and no user interaction required. By sending multiple concurrent requests to the get_response() method, attackers can fully deplete the connection pool, resulting in high-impact availability disruption without affecting confidentiality or integrity.
ChatterBot version 1.2.11 resolves the issue through fixes detailed in the associated GitHub commit (de89fe648139f8eeacc998ad4524fab291a378cf), pull request 2432, and release notes. The GitHub security advisory (GHSA-v4w8-49pv-mf72) provides further details on the vulnerability and mitigation. Security practitioners should upgrade to version 1.2.11 immediately to prevent exploitation.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-3299
Vulnerability Data
ChatterBot is a machine learning, conversational dialog engine for creating chat bots. ChatterBot versions up to 1.2.10 are vulnerable to a denial-of-service condition caused by improper database session and connection pool management. Concurrent invocations of the get_response() method can exhaust…
more
the underlying SQLAlchemy connection pool, resulting in persistent service unavailability and requiring a manual restart to recover. Version 1.2.11 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: machine learning
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Process isolation confines resource consumption to separate domains, reducing blast radius without stopping the root flaw.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.
Early notification of anomalous resource consumption or system malfunctions enables throttling or isolation before availability is lost.