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Passing Tests Is Not Enough: Code Agents Can Be Steered Into Vulnerable Fixes

Autonomous coding agents, which read a bug report, edit a code repository, and submit a fix, are increasingly trusted with real software maintenance. Their evaluations, however, mostly ask one question: does the patch pass the test suite? A new study by researchers at Carnegie Mellon University and several collaborating institutions suggests that a patch can pass every test while introducing a security flaw, and that an attacker can make this more likely with a single, ordinary-looking issue report.

The authors call such code “Functionally Correct yet Vulnerable” (FCV) patches. Vulnerabilities are judged against the Common Weakness Enumeration (CWE), a standard catalog of software weakness types. Even without any attack, the team found that 4.3% to 6.0% of functionally correct patches from the Mini-SWE-Agent framework contained CWE-defined weaknesses, depending on the underlying model.

To test whether this risk can be amplified, the researchers developed the FCV-Attack. It appends a developer-style suggestion to a GitHub issue, framed around a plausible goal such as flexibility or better logging, and names a target weakness type. The attacker needs only black-box access, meaning no model weights or internal tools, and a single query. The authors argue this threat is realistic: a malicious contributor could post such text, or a benign developer could paste it from a tutorial or forum.

The paper offers a concrete illustration. An issue asked for a fix to a crash when loading malformed inputs. The injected text added a suggestion to use eval for “dynamic” processing of user input. The resulting patch replaced a safe parser with a call to eval, which allowed arbitrary code execution (CWE-94). The patch still resolved the reported crash and passed the tests.

Across 12 combinations of four models and three agent frameworks on SWE-Bench, the attack succeeded at least once in every configuration. The strongest results involved CWE-538, which concerns inserting sensitive information into accessible locations, such as logging credentials. The attack reached 40.7% on GPT-5 mini with OpenHands and 55.6% on Claude Sonnet 4 with OpenHands. The authors suggest logging looks like a harmless debugging request, whereas eval is something agents are trained to avoid. They also report that more capable, instruction-following models were not safer: average attack success was 14.0% for Claude Sonnet 4 and 13.7% for GPT-5 mini, compared with 8.3% for Qwen3-Coder.

A second experiment replayed clean recorded agent trajectories with the malicious instruction inserted at the start, and vulnerabilities still appeared. For Kimi-K2-Instruct on CWE-538, the rate was 47.5%, compared with 54.2% under the standard attack. The authors attribute this to the injected text persisting in the model’s internal key-value cache, which would mean behavior-monitoring defenses are insufficient. They acknowledge, however, that they inferred this from external behavior and did not examine the model’s internal representations.

A simple defense, adding one sentence to the system prompt asking the agent to avoid risky patterns, lowered the CWE-538 attack success for Kimi-K2-Instruct from 54.2% to 43.3%. That remains far above the 0.8% clean baseline.

The study has clear boundaries. It covers four CWE types, a single query, and SWE-Bench tasks the agents could already solve cleanly. Vulnerabilities were identified by an LLM judge, Qwen3-Coder, rather than by a security audit. Real repositories and human-agent collaboration may behave differently.

Even so, the work suggests that evaluations centered on passing tests may miss a class of security failures, and that defenses will need to examine the security of the code agents produce, not just their outcomes.