The Trojan Horse paper introduces a pipeline for selecting reachable locations in repository code, mounting malicious payloads and measuring whether a coding agent carries those effects through legitimate task work. Its TrojanBench dataset contains 384 poisoned repositories across multiple programming languages and harnesses. The attack succeeds when model…
Inspect the ClaimsCase · DiggingBeagle record
TrojanForge locates runtime-reachable code paths for repository poisoning of coding agents
A controlled benchmark places malicious changes into repositories where a coding agent may later execute or propagate them.
Researcher-controlled repository dataset and coding-agent harness evaluations, October 2026; no named production victims.
Assessment profile
Assessment method- Setting
- evaluation
- Exploitation
- demonstrated
- Evidence
- Grade B
- Remediation
- not applicable
- AI role
- AGAINST AI
Basis and provenance
Technical report or researcher-controlled experiments document the stated mechanism, but independent reproduction and population-wide prevalence are not established. Impact band is left unassigned because no calibrated damage threshold or representative victim impact is supported by the available record.
Assessed Oct 20, 2026 using diggingbeagle.assessment/1.
Evidence basis: CLM-TROJANFORGE-DATA · CLM-TROJANFORGE-REALM · Trojan Horse automated repository poisoning against coding agents
At a glance
Code review and independent execution checks are needed at the point a coding agent promotes repository contents into trusted commands or changes.
Read the implicationsRobustness under unseen repositories and independent unauthorized-effect validation remains uncertain.
Limits and uncertaintyFull account
The Trojan Horse paper introduces a pipeline for selecting reachable locations in repository code, mounting malicious payloads and measuring whether a coding agent carries those effects through legitimate task work. Its TrojanBench dataset contains 384 poisoned repositories across multiple programming languages and harnesses. The attack succeeds when model behavior or copied code crosses the boundary between untrusted project material and an authorized build, test or execution step.
Reported success rates vary by benchmark setup and whether the measured outcome is placement, execution, or a validated malicious effect. This is not evidence that 384 production repositories were attacked or that all coding agents are equally vulnerable. Repository origin, executable hooks and runtime effects need explicit inspection; a syntactically plausible change can be untrustworthy even when no prompt tells the model to disobey its user.
The attacker works through repository content selected for likely runtime reachability. Successful injection, executed code and verified malicious effect are successive stages and should use separate measurements. A poisoned source file that is never imported has different impact from a changed dependency that a coding agent executes during a legitimate test. The published dataset is an experiment, not a list of affected public repositories.
Timeline
- Oct 19, 2026disclosure
Public disclosure
Claims & evidence
CLM-TROJANFORGE-DATATrojanBench contains 384 researcher-poisoned repositories and evaluates automated attack placement in coding-agent workflows.supported
Basis: reported finding
- supportsTrojan Horse automated repository poisoning against coding agentsprimary disclosure
Abstract; TrojanBench construction; experiments
CLM-TROJANFORGE-REALMThe reported high attack rates are laboratory outcomes, not verified compromise counts in production code repositories.supported
Basis: reported finding
- supportsTrojan Horse automated repository poisoning against coding agentsprimary disclosure
Experimental setup; Discussion; Real-World Validation limitations
Implications
Code review and independent execution checks are needed at the point a coding agent promotes repository contents into trusted commands or changes.
Unknowns and contradictions
- Robustness under unseen repositories and independent unauthorized-effect validation remains uncertain.
Sources and citation
Material revision history
- Oct 20, 2026 · Canonical change recorded · new in release · revision 177