Security researchers have documented two distinct attack patterns targeting autonomous AI coding agents — and both exploit behaviors that are features, not bugs. That’s what makes them especially troubling.

Friendly Fire, demonstrated by researchers at the AI Now Institute including Chief AI Scientist Heidy Khlaaf, inverts the defensive purpose of code-scanning agents entirely. HalluSquatting, from researchers at Tel Aviv University, the Technion, and Intuit, weaponizes the reliable hallucination behavior of large language models to pre-position malware at package names agents will invent on their own. Together, they represent a maturing discipline of attacker-specific AI agent exploitation.

Friendly Fire: When the Defender Becomes the Weapon

The scenario Friendly Fire exploits is one of the most straightforward and widely promoted uses of AI coding agents: scanning third-party or open-source code for vulnerabilities. Security teams use Claude Code, Codex, and similar tools to automatically review unfamiliar codebases — a task that would take human analysts considerably longer and that these tools perform with impressive efficiency.

The attack works by embedding malicious content in the code being scanned. When an agent operating in autonomous or reduced-supervision mode reads the crafted files, it can be tricked into executing attacker-supplied code rather than (or in addition to) analyzing it. The scanning task itself — initiated for defensive purposes — becomes the initial access vector.

This is a particularly elegant inversion. The attacker doesn’t need to compromise the agent’s infrastructure, steal credentials, or find a traditional software vulnerability. They just need to place a crafted file where a defensive scan will encounter it. The agent does the rest.

Friendly Fire was demonstrated against Claude Sonnet and Opus variants, as well as GPT-5.5-based Codex operating in autonomous mode. The researchers’ core finding is uncomfortable but important: the autonomous code execution privileges that make these agents useful for security scanning are exactly what makes them exploitable as delivery mechanisms.

Key risk factors for Friendly Fire

  • Agent running in auto-approve or “yolo” mode (skip-permissions) with no human review of individual commands
  • Agent has shell execution or file modification capabilities
  • The codebase being scanned is from an untrusted or external source

All three conditions are common in CI/CD pipelines where AI scanning is deployed for speed.

HalluSquatting: Turning Hallucination Into an Attack Surface

HalluSquatting takes a different approach. The research team — Ben Nassi’s group at Tel Aviv University alongside Technion and Intuit researchers — started from an observation that has been documented for years: LLMs hallucinate package names, repository names, and skill names. When asked to fetch a “popular” or “trending” resource, models will often invent plausible-sounding names that don’t exist in any real registry.

The key insight for HalluSquatting is that hallucinations are predictable and consistent. Researchers found that for certain request phrasings, models would hallucinate the same fake package name at rates of 85–100% across multiple runs and multiple model variants. Once you can reliably predict what a model will hallucinate, you can register that exact name on GitHub, npm, PyPI, or skill stores — and seed it with malicious payloads.

The attack chain:

  1. Query AI agents repeatedly to map which package/skill names get hallucinated for specific requests
  2. Register those exact names on real registries with adversarial content
  3. Wait for users to ask their AI agent to fetch or install the resource
  4. The agent hallucinates the attacker-controlled name, fetches it, and — in autonomous mode — executes the hidden instructions

Demonstrated against at least nine tools including Cursor, Cursor CLI, Gemini CLI, Windsurf, GitHub Copilot, Cline, and several OpenClaw variants, the research showed agents could be turned into botnet nodes, ransomware delivery vectors, or persistent remote access tools through this pathway.

This is conceptually related to earlier “slopsquatting” or “phantom squatting” research, but HalluSquatting extends it specifically to agentic contexts where fetch-and-run behaviors are common and often under-supervised.

What Both Attacks Have in Common

Despite using different mechanics, Friendly Fire and HalluSquatting share a structural DNA. Both:

  • Exploit autonomous execution: The attacks only work (or work most effectively) when agents can take actions without per-action human approval
  • Leverage expected, normal behaviors: Code scanning is supposed to read files; package fetching is supposed to retrieve from external sources. The attacks embed themselves in the expected workflow
  • Scale with agent capability: As agents become more capable and autonomous, both attack surfaces grow

There’s also a sobering implication for the AI safety ecosystem: HITL (Human-in-the-Loop) safety models are commonly cited as the primary guardrail for agentic systems. But if the information presented for approval is wrong (GhostApproval), or if the human never sees individual actions at all (Friendly Fire, HalluSquatting in auto-mode), the loop is effectively broken.

Practical Defenses Right Now

Neither attack requires patched software from vendors — the mitigations are primarily operational:

Against Friendly Fire:

  • Disable auto-approve or “yolo” modes when scanning untrusted or externally-sourced code
  • Require human approval for any shell execution or file modification during security scanning tasks
  • Run scanning in sandboxed environments with limited write access to the host filesystem

Against HalluSquatting:

  • Before an AI agent fetches or installs any package, verify the name exists in the real registry first — treat AI-suggested dependency names as unverified guesses
  • Implement allowlists for packages and repositories that agents can access without additional confirmation
  • Review any new dependency that appears in agent-generated code or configuration before allowing it into production
  • On platforms that support it, enable monitoring of agent-suggested dependencies against known legitimate registry contents

Systemic considerations: The platform-level response — pre-registering commonly hallucinated names to prevent attacker squatting — is being explored, but it’s a cat-and-mouse problem given the breadth of possible hallucination targets across different request phrasings and model variants.

For teams running AI agents in CI/CD or automated security workflows, now is a good time to audit what execution permissions agents hold and whether autonomous approval modes are actually necessary for each workload.

Sources

  1. Vulnerability Intelligence Report, July 9, 2026 — Threat-Modeling.com
  2. New HalluSquatting Attack Could Trick AI Coding Assistants — The Hacker News
  3. Heidy Khlaaf — AI Now Institute
  4. HalluSquatting Attack Research Overview — AIChatDaily
  5. AI Package Hallucinations — Lasso Security

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260713-2000

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